Normalized plot styles across chapters.

I had a lot of problems with lines being visible, and ended up
using different styles in different places. I found a style that
seems to work everywhere and put the code in book_plots.py.

01_g-h_filter.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:c072eb076dee3ffbdd6cfe02a570292c00848b0aa38c419ab3f0f7c08f2bf392"
+  "signature": "sha256:a976736be24f72af59769341dfd37ad1abad6c84a0de77c22512e24bb78eb8d7"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -259,7 +259,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7fc14f36d6a0>"
+        "<IPython.core.display.HTML at 0x7f25ddc7e6a0>"
        ]
       }
      ],
@@ -321,7 +321,7 @@
       "plt.errorbar([170], [1.05], xerr=8, fmt='o', label='B', capthick=2, capsize=10)\n",
       "plt.ylim(0, 2)\n",
       "plt.xlim(145, 185)\n",
-      "plt.legend(scatterpoints=3)\n",
+      "plt.legend()\n",
       "plt.gca().axes.yaxis.set_ticks([])\n",
       "plt.show()"
      ],
@@ -331,9 +331,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAxUAAAGGCAYAAAANcKzOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAxUAAAGGCAYAAAANcKzOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc1324ae518>"
+        "<matplotlib.figure.Figure at 0x7f25c17c66a0>"
        ]
       }
      ],
@@ -368,9 +368,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc1324aeba8>"
+        "<matplotlib.figure.Figure at 0x7f25c14799b0>"
        ]
       }
      ],
@@ -402,9 +402,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc1324ae358>"
+        "<matplotlib.figure.Figure at 0x7f25c17c6588>"
        ]
       }
      ],
@@ -446,7 +446,7 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "Average of measurements is 165.0624\n"
+        "Average of measurements is 165.0186\n"
        ]
       }
      ],
@@ -482,7 +482,7 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "Average of measurements is 164.9074\n"
+        "Average of measurements is 164.9325\n"
        ]
       }
      ],
@@ -523,9 +523,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc132372f98>"
+        "<matplotlib.figure.Figure at 0x7f25c140d208>"
        ]
       }
      ],
@@ -559,9 +559,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc1323b3f98>"
+        "<matplotlib.figure.Figure at 0x7f25c13ff080>"
        ]
       }
      ],
@@ -594,9 +594,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc1322a1828>"
+        "<matplotlib.figure.Figure at 0x7f25c13a4a58>"
        ]
       }
      ],
@@ -632,9 +632,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc13247a710>"
+        "<matplotlib.figure.Figure at 0x7f25c1451358>"
        ]
       }
      ],
@@ -670,9 +670,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc13223cac8>"
+        "<matplotlib.figure.Figure at 0x7f25c129ae48>"
        ]
       }
      ],
@@ -709,9 +709,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc1323775f8>"
+        "<matplotlib.figure.Figure at 0x7f25c14249b0>"
        ]
       }
      ],
@@ -736,9 +736,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc132150080>"
+        "<matplotlib.figure.Figure at 0x7f25c11a4dd8>"
        ]
       }
      ],
@@ -776,9 +776,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc1321c2e10>"
+        "<matplotlib.figure.Figure at 0x7f25c12b6f98>"
        ]
       }
      ],
@@ -802,6 +802,8 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
+      "import book_plots as bp\n",
+      "\n",
       "weights = [158.0, 164.2, 160.3, 159.9, 162.1, 164.6, \n",
       "           169.6, 167.4, 166.4, 171.0, 171.2, 172.6]\n",
       "\n",
@@ -828,9 +830,9 @@
       "    n = len(weights)\n",
       "    plt.xlim([1, n])\n",
       "\n",
-      "    plt.plot (range(1, n+1), estimates, '--', label='filter')\n",
-      "    plt.plot(range(1, n+1), weights, c='r', label='measurements')\n",
-      "    plt.plot([1, n],[160, 160+n],c='g', label='actual')\n",
+      "    bp.plot_filter(range(1, n+1), estimates)\n",
+      "    bp.plot_measurements(range(1, n+1), weights, label='Scale')\n",
+      "    bp.plot_track([1, n],[160, 160+n], label='Actual Weight')\n",
       "    plt.legend(loc=2)\n",
       "    plt.xlabel('day')\n",
       "    plt.ylabel('weight (lbs)')\n",
@@ -844,9 +846,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc13237a588>"
+        "<matplotlib.figure.Figure at 0x7f25c14e90b8>"
        ]
       }
      ],
@@ -856,7 +858,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "That is pretty good! The blue dots, showing our estimates, are not a straight line, but they are straighter than the measurements and somewhat close to the trend line we created. Also, it seems to get better over time. \n",
+      "That is pretty good! The dotted blue lines, showing our estimates, are not a straight line, but they are straighter than the measurements and somewhat close to the trend line we created. Also, it seems to get better over time. \n",
       "\n",
       "This may strike you as quite silly; of course the data will look good if we assume the conclusion, that our weight gain is around 1 lb/day! Let's see what the filter does if our initial guess is bad. Let's see what happens if I predict that there is no weight gain."
      ]
@@ -873,9 +875,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc132166908>"
+        "<matplotlib.figure.Figure at 0x7f25c13ae0b8>"
        ]
       }
      ],
@@ -929,10 +931,11 @@
       "# plot results\n",
       "n = len(weights)\n",
       "plt.xlim([1, n])\n",
-      "    \n",
-      "plt.plot(range(1, n+1), estimates, '--', label='filter')\n",
-      "plt.plot(range(1, n+1), weights, label='measurements')\n",
-      "plt.plot([1 ,n], [160,160+n], label='actual')\n",
+      "   \n",
+      "days = np.linspace(1, n, n)\n",
+      "bp.plot_filter(days, estimates)\n",
+      "bp.plot_measurements(days, weights, label='Scale')\n",
+      "bp.plot_track([1, n], [160, 160+n], label='Actual weight')\n",
       "plt.legend(loc=2)\n",
       "plt.xlabel('day')\n",
       "plt.ylabel('weight (lbs)')\n",
@@ -944,9 +947,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc132213320>"
+        "<matplotlib.figure.Figure at 0x7f25c1514828>"
        ]
       }
      ],
@@ -1010,7 +1013,7 @@
        "output_type": "display_data",
        "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7fc1324084a8>"
+        "<matplotlib.figure.Figure at 0x7f25c13cb710>"
        ]
       }
      ],
@@ -1126,20 +1129,23 @@
       "        \n",
       "        results.append(x)\n",
       "        \n",
-      "    return results\n",
+      "    return np.asarray(results)\n",
       "\n",
-      "def plot_g_h_results(measurements, filtered_data, title=''):    \n",
-      "    plt.plot(filtered_data, '--',label='filter')\n",
-      "    plt.plot(measurements,label='measurement')\n",
+      "def plot_g_h_results(measurements, filtered_data, \n",
+      "                     title='', z_label='Scale', ):    \n",
+      "    bp.plot_measurements(measurements, label=z_label)\n",
+      "    bp.plot_filter(filtered_data)\n",
       "    plt.legend(loc=4)\n",
       "    plt.title(title)\n",
+      "    plt.gca().set_xlim(left=0,right=len(measurements))\n",
       "    plt.show()\n",
       "\n",
       "\n",
       "plt.xlim([0,10])\n",
-      "plt.plot([0,9],[160,170],label='actual')\n",
+      "bp.plot_track([0,11],[160,172],label='Actual weight')\n",
       "data = g_h_filter(data=weights, x0=160, dx=1, g=6./10, h=2./3, dt=1.)\n",
-      "plot_g_h_results(weights, data)"
+      "plot_g_h_results(weights, data)\n",
+      "print(len(weights))"
      ],
      "language": "python",
      "metadata": {},
@@ -1147,9 +1153,16 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc13214eda0>"
+        "<matplotlib.figure.Figure at 0x7f25c1398898>"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "12\n"
        ]
       }
      ],
@@ -1226,9 +1239,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc132157128>"
+        "<matplotlib.figure.Figure at 0x7f25c120db00>"
        ]
       }
      ],
@@ -1282,9 +1295,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc132141d68>"
+        "<matplotlib.figure.Figure at 0x7f25c1414ba8>"
        ]
       }
      ],
@@ -1345,9 +1358,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc1321ce358>"
+        "<matplotlib.figure.Figure at 0x7f25c11e2978>"
        ]
       }
      ],
@@ -1419,9 +1432,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc132235160>"
+        "<matplotlib.figure.Figure at 0x7f25c1442a90>"
        ]
       }
      ],
@@ -1494,25 +1507,25 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc132378128>"
+        "<matplotlib.figure.Figure at 0x7f25c1220a90>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc1323782e8>"
+        "<matplotlib.figure.Figure at 0x7f25c15262b0>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc13225d4a8>"
+        "<matplotlib.figure.Figure at 0x7f25c11b2160>"
        ]
       }
      ],
@@ -1549,25 +1562,25 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc132277550>"
+        "<matplotlib.figure.Figure at 0x7f25c118ed30>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc132145da0>"
+        "<matplotlib.figure.Figure at 0x7f25c11c8710>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7fc13220eef0>"
+        "<matplotlib.figure.Figure at 0x7f25c118e9b0>"
        ]
       }
      ],
@@ -1600,17 +1613,17 @@
       {
        "metadata": {},
        "output_type": "display_data",
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        "text": [
-        "<matplotlib.figure.Figure at 0x7fc1322775f8>"
+        "<matplotlib.figure.Figure at 0x7f25c1233828>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc1321b7630>"
+        "<matplotlib.figure.Figure at 0x7f25c11a4fd0>"
        ]
       }
      ],
@@ -1656,25 +1669,25 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7fc130895b00>"
+        "<matplotlib.figure.Figure at 0x7f25c00beb00>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc130895ac8>"
+        "<matplotlib.figure.Figure at 0x7f25c00c3d30>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc132363080>"
+        "<matplotlib.figure.Figure at 0x7f25c1262eb8>"
        ]
       }
      ],
@@ -1741,8 +1754,9 @@
       "vel = 15\n",
       "zs = gen_train_data(pos, vel, 100)\n",
       "\n",
-      "plt.plot(np.asarray(zs)/1000)# convert to km\n",
+      "plt.plot(zs/1000)# convert to km\n",
       "plt.xlabel('time(sec)'); plt.ylabel('km')\n",
+      "plt.title('Train Position')\n",
       "plt.show()"
      ],
      "language": "python",
@@ -1751,9 +1765,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc132335518>"
+        "<matplotlib.figure.Figure at 0x7f25c11a4048>"
        ]
       }
      ],
@@ -1774,7 +1788,7 @@
      "input": [
       "zs = gen_train_data(23000, 15, 100)\n",
       "data = g_h_filter(data=zs, x0=23000, dx=15., dt=1.,g=.01, h=0.0001)\n",
-      "plot_g_h_results(zs/1000, np.asarray(data)/1000, 'g=0.01, h=0.0001')"
+      "plot_g_h_results(zs/1000, data/1000, 'g=0.01, h=0.0001', z_label='Measurements')"
      ],
      "language": "python",
      "metadata": {},
@@ -1782,9 +1796,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc13222d5f8>"
+        "<matplotlib.figure.Figure at 0x7f25c11ddb70>"
        ]
       }
      ],
@@ -1803,7 +1817,7 @@
      "input": [
       "zs = gen_train_data(23000, 15, 100)\n",
       "data = g_h_filter(data=zs, x0=23000, dx=15., dt=1.,g=.2, h=0.0001)\n",
-      "plot_g_h_results(zs/1000, np.asarray(data)/1000, 'g=0.2, h=0.0001')"
+      "plot_g_h_results(zs/1000, data/1000, 'g=0.2, h=0.0001', z_label='Measurements')"
      ],
      "language": "python",
      "metadata": {},
@@ -1811,9 +1825,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc132403ef0>"
+        "<matplotlib.figure.Figure at 0x7f25c145f9e8>"
        ]
       }
      ],
@@ -1834,7 +1848,7 @@
      "input": [
       "zs = gen_train_data(23000, 15, 100)\n",
       "data = g_h_filter(data=zs, x0=23000, dx=15., dt=1., g=0.01, h=0.1)\n",
-      "plot_g_h_results(zs/1000, np.asarray(data)/1000, 'g=0.01, h=0.1')"
+      "plot_g_h_results(zs/1000, data/1000, 'g=0.01, h=0.1', z_label='Measurements')"
      ],
      "language": "python",
      "metadata": {},
@@ -1842,9 +1856,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc1323f5518>"
+        "<matplotlib.figure.Figure at 0x7f25c11f92e8>"
        ]
       }
      ],
@@ -1882,7 +1896,7 @@
      "input": [
       "zs = gen_train_data_with_acc(23000, 15, 100)\n",
       "data = g_h_filter(data=zs, x0=23000, dx=15., dt=1.,g=.01, h=0.0001)\n",
-      "plot_g_h_results(zs/1000, np.asarray(data)/1000, 'g=0.01, h=0.0001')"
+      "plot_g_h_results(zs/1000, data/1000, 'g=0.01, h=0.0001', z_label='Measurements')"
      ],
      "language": "python",
      "metadata": {},
@@ -1890,9 +1904,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc13219b7f0>"
+        "<matplotlib.figure.Figure at 0x7f25c1372048>"
        ]
       }
      ],
@@ -1911,7 +1925,7 @@
      "input": [
       "zs = gen_train_data_with_acc(23000, 15, 100)\n",
       "data = g_h_filter(data=zs, x0=23000, dx=15., dt=1.,g=.01, h=0.001)\n",
-      "plot_g_h_results(zs/1000, np.asarray(data)/1000, 'g=0.01, h=0.001')"
+      "plot_g_h_results(zs/1000, data/1000, 'g=0.01, h=0.001', z_label='Measurements')"
      ],
      "language": "python",
      "metadata": {},
@@ -1919,9 +1933,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAysAAAGaCAYAAAAPXtylAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc13218abe0>"
+        "<matplotlib.figure.Figure at 0x7f25c008e828>"
        ]
       }
      ],

02_Discrete_Bayes.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:161cde2172ad51a96760453968bfc1209c772d9e287deb485626173c4e558e4f"
+  "signature": "sha256:ed27079cf7cc6b818d3cd0af27d1a7dafbe17aa5469f61f0a49355a3d1a14ae1"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -259,7 +259,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7ff9e3a526a0>"
+        "<IPython.core.display.HTML at 0x7f3e37ae46a0>"
        ]
       }
      ],
@@ -345,11 +345,11 @@
      "input": [
       "from __future__ import print_function, division\n",
       "import matplotlib.pyplot as plt\n",
-      "import bar_plot\n",
+      "import book_plots as bp\n",
       "import numpy as np\n",
       "\n",
       "pos_belief = np.array([1./3, 1./3, 0, 0, 0, 0, 0, 0, 1./3, 0])\n",
-      "bar_plot.plot(pos_belief)"
+      "bp.bar_plot(pos_belief)"
      ],
      "language": "python",
      "metadata": {},
@@ -357,9 +357,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAxYAAAGNCAYAAACMgOcOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ff9c6e89eb8>"
+        "<matplotlib.figure.Figure at 0x7f3e1b60d908>"
        ]
       }
      ],
@@ -484,7 +484,7 @@
       "\n",
       "print(pos_belief)\n",
       "print('sum =', sum(pos_belief))\n",
-      "bar_plot.plot(pos_belief)"
+      "bp.bar_plot(pos_belief)"
      ],
      "language": "python",
      "metadata": {},
@@ -500,9 +500,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAxYAAAGNCAYAAACMgOcOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ff9e3a570b8>"
+        "<matplotlib.figure.Figure at 0x7f3e1b369a58>"
        ]
       }
      ],
@@ -535,7 +535,7 @@
       "print('sum =', sum(pos_belief))\n",
       "print('probability of door =', pos_belief[0])\n",
       "print('probability of wall =', pos_belief[2])\n",
-      "bar_plot.plot(pos_belief)"
+      "bp.bar_plot(pos_belief)"
      ],
      "language": "python",
      "metadata": {},
@@ -552,9 +552,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAxYAAAGNCAYAAACMgOcOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAxYAAAGNCAYAAACMgOcOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ff9c6aea748>"
+        "<matplotlib.figure.Figure at 0x7f3e1b25ab00>"
        ]
       }
      ],
@@ -605,11 +605,11 @@
       "        \n",
       "pos_belief = np.array([.4, .1, .2, .3])\n",
       "print('belief before predict =', pos_belief)\n",
-      "bar_plot.plot (pos_belief, title='Before prediction')\n",
+      "bp.bar_plot(pos_belief, title='Before prediction')\n",
       "\n",
       "perfect_predict(pos_belief, 1)\n",
       "print('belief after predict =', pos_belief)\n",
-      "bar_plot.plot(pos_belief, title='After prediction')"
+      "bp.bar_plot(pos_belief, title='After prediction')"
      ],
      "language": "python",
      "metadata": {},
@@ -632,9 +632,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAxYAAAGaCAYAAACSU9UtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ff9c6a27ac8>"
+        "<matplotlib.figure.Figure at 0x7f3e1b26c828>"
        ]
       }
      ],
@@ -683,7 +683,7 @@
       "pos_belief = np.array([0, 0, 0, 1, 0, 0, 0, 0], dtype=float)\n",
       "predict(pos_belief, 2, .8, .1, .1)\n",
       "print(pos_belief)\n",
-      "bar_plot.plot(pos_belief)"
+      "bp.bar_plot(pos_belief)"
      ],
      "language": "python",
      "metadata": {},
@@ -698,9 +698,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAxYAAAGNCAYAAACMgOcOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ff9c6a38668>"
+        "<matplotlib.figure.Figure at 0x7f3e1b21e5c0>"
        ]
       }
      ],
@@ -720,7 +720,7 @@
       "pos_belief = np.array([0, 0, .4, .6, 0, 0, 0, 0])\n",
       "predict(pos_belief, 2, .8, .1, .1)\n",
       "print(pos_belief)\n",
-      "bar_plot.plot(pos_belief)"
+      "bp.bar_plot(pos_belief)"
      ],
      "language": "python",
      "metadata": {},
@@ -735,9 +735,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAxYAAAGNCAYAAACMgOcOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAxYAAAGNCAYAAACMgOcOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ff9c6af2c50>"
+        "<matplotlib.figure.Figure at 0x7f3e1b216748>"
        ]
       }
      ],
@@ -756,7 +756,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "bar_plot.plot(pos_belief)"
+      "bp.bar_plot(pos_belief)"
      ],
      "language": "python",
      "metadata": {},
@@ -764,9 +764,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAxYAAAGNCAYAAACMgOcOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ff9d1447fd0>"
+        "<matplotlib.figure.Figure at 0x7f3e1b5f8e10>"
        ]
       }
      ],
@@ -787,7 +787,7 @@
       "for i in range (500):\n",
       "    predict(pos_belief, 1, .8, .1, .1)\n",
       "print(pos_belief)\n",
-      "bar_plot.plot(pos_belief)"
+      "bp.bar_plot(pos_belief)"
      ],
      "language": "python",
      "metadata": {},
@@ -802,9 +802,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAxYAAAGNCAYAAACMgOcOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ff9c6aa3320>"
+        "<matplotlib.figure.Figure at 0x7f3e1b1a59e8>"
        ]
       }
      ],
@@ -874,7 +874,7 @@
      "input": [
       "predict(pos_belief, 1, .8, .1, .1)\n",
       "print(pos_belief)\n",
-      "bar_plot.plot(pos_belief)"
+      "bp.bar_plot(pos_belief)"
      ],
      "language": "python",
      "metadata": {},
@@ -889,9 +889,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAxYAAAGNCAYAAACMgOcOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ff9c6aa39e8>"
+        "<matplotlib.figure.Figure at 0x7f3e37ae75f8>"
        ]
       }
      ],
@@ -910,7 +910,7 @@
      "input": [
       "update(pos_belief, 1, .6, .2)\n",
       "print(pos_belief)\n",
-      "bar_plot.plot(pos_belief)"
+      "bp.bar_plot(pos_belief)"
      ],
      "language": "python",
      "metadata": {},
@@ -925,9 +925,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAxYAAAGNCAYAAACMgOcOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ff9c687e978>"
+        "<matplotlib.figure.Figure at 0x7f3e1b27d940>"
        ]
       }
      ],
@@ -946,7 +946,7 @@
      "input": [
       "predict(pos_belief, 1, .8, .1, .1)\n",
       "update(pos_belief, 0, .6, .2)\n",
-      "bar_plot.plot(pos_belief)"
+      "bp.bar_plot(pos_belief)"
      ],
      "language": "python",
      "metadata": {},
@@ -954,9 +954,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAxYAAAGNCAYAAACMgOcOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ff9c6a05400>"
+        "<matplotlib.figure.Figure at 0x7f3e1b313908>"
        ]
       }
      ],
@@ -975,7 +975,7 @@
      "input": [
       "predict(pos_belief, 1, .8, .1, .1)\n",
       "update(pos_belief, 0, .6, .2)\n",
-      "bar_plot.plot(pos_belief)"
+      "bp.bar_plot(pos_belief)"
      ],
      "language": "python",
      "metadata": {},
@@ -983,9 +983,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAxYAAAGNCAYAAACMgOcOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAxYAAAGNCAYAAACMgOcOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ff9c6a5eb38>"
+        "<matplotlib.figure.Figure at 0x7f3e1b0be748>"
        ]
       }
      ],
@@ -1034,7 +1034,7 @@
       "for m in measurements:\n",
       "    update(pos_belief, m, .6, .2)\n",
       "    predict(pos_belief, 1, .8, .1, .1)\n",
-      "bar_plot.plot(pos_belief)\n",
+      "bp.bar_plot(pos_belief)\n",
       "print(pos_belief)"
      ],
      "language": "python",
@@ -1050,9 +1050,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAxYAAAGNCAYAAACMgOcOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ff9c69e43c8>"
+        "<matplotlib.figure.Figure at 0x7f3e1b23c080>"
        ]
       }
      ],
@@ -1071,7 +1071,7 @@
      "input": [
       "update(pos_belief, m, .6, .2)\n",
       "predict(pos_belief, 1, .8, .1, .1)\n",
-      "bar_plot.plot(pos_belief)"
+      "bp.bar_plot(pos_belief)"
      ],
      "language": "python",
      "metadata": {},
@@ -1079,9 +1079,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAxYAAAGNCAYAAACMgOcOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ff9c6b70cc0>"
+        "<matplotlib.figure.Figure at 0x7f3e1b194b00>"
        ]
       }
      ],
@@ -1104,7 +1104,7 @@
       "    update(pos_belief, m, .6, .2)\n",
       "    predict(pos_belief, 1, .8, .1, .1)\n",
       "    plt.subplot(3, 2, i+1)\n",
-      "    bar_plot.plot(pos_belief, title='step{}'.format(i+1))"
+      "    bp.bar_plot(pos_belief, title='step{}'.format(i+1))"
      ],
      "language": "python",
      "metadata": {},
@@ -1112,9 +1112,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAxYAAAGaCAYAAACSU9UtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAxYAAAGaCAYAAACSU9UtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ff9c683c358>"
+        "<matplotlib.figure.Figure at 0x7f3e1b0241d0>"
        ]
       }
      ],
@@ -1158,7 +1158,7 @@
      "input": [
       "pos_belief = [0.2245871, 0.06288015, 0.06109133, 0.0581008, 0.09334062, \n",
       "              0.2245871, 0.06288015, 0.06109133, 0.0581008, 0.09334062]\n",
-      "bar_plot.plot(pos_belief)  "
+      "bp.bar_plot(pos_belief)  "
      ],
      "language": "python",
      "metadata": {},
@@ -1166,9 +1166,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ff9c6827208>"
+        "<matplotlib.figure.Figure at 0x7f3e1b004a58>"
        ]
       }
      ],

04_Gaussians.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:2130dc4c97d74b7134afa090e42b6b505dc965f30bb05abe569a3168f652856d"
+  "signature": "sha256:bb9a8f2c142d326ed76d662b23ad42518b72c5dc661e7861848dbcbae2bb8a2f"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -259,7 +259,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7f194f549b38>"
+        "<IPython.core.display.HTML at 0x7f7c7d8396a0>"
        ]
       }
      ],
@@ -298,9 +298,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1948330128>"
+        "<matplotlib.figure.Figure at 0x7f7c5d274cc0>"
        ]
       }
      ],
@@ -344,9 +344,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1947b15b70>"
+        "<matplotlib.figure.Figure at 0x7f7c5d06ee10>"
        ]
       }
      ],
@@ -421,9 +421,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1947ab20b8>"
+        "<matplotlib.figure.Figure at 0x7f7c5cff8fd0>"
        ]
       }
      ],
@@ -552,9 +552,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1947a215c0>"
+        "<matplotlib.figure.Figure at 0x7f7c595a2f28>"
        ]
       }
      ],
@@ -594,9 +594,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f196c12ee80>"
+        "<matplotlib.figure.Figure at 0x7f7c595a2b38>"
        ]
       }
      ],
@@ -606,9 +606,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "<p>\n",
-      "<p>\n",
-      "<div style=\"border:1px dotted black;background-color:#DCDCDC#\">**Sidebar**: An equivalent formation for a Gaussian is $\\mathcal{N}(\\mu,1/\\tau)$ where $\\mu$ is the *mean* and $tau$ the *precision*. Here $1/\\tau = \\sigma^2$; it is the reciprocal of the variance. While we do not use this formulation in this book, it underscores that the variance is a measure of how precise our data is. A small variance yields large precision - our measurement is very precise. Conversely, a large variance yields low precision - our belief is spread out across a large area. You should become comfortable with thinking about Gaussians in these equivalent forms. Gaussians reflect our *belief* about a measurement, they express the *precision* of the measurement, and they express how much *variance* there is in the measurements. These are all different ways of stating the same fact.</div>"
+      "> An equivalent formation for a Gaussian is $\\mathcal{N}(\\mu,1/\\tau)$ where $\\mu$ is the *mean* and $tau$ the *precision*. Here $1/\\tau = \\sigma^2$; it is the reciprocal of the variance. While we do not use this formulation in this book, it underscores that the variance is a measure of how precise our data is. A small variance yields large precision - our measurement is very precise. Conversely, a large variance yields low precision - our belief is spread out across a large area. You should become comfortable with thinking about Gaussians in these equivalent forms. Gaussians reflect our *belief* about a measurement, they express the *precision* of the measurement, and they express how much *variance* there is in the measurements. These are all different ways of stating the same fact.</div>"
      ]
     },
     {
@@ -650,9 +648,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f19477dff28>"
+        "<matplotlib.figure.Figure at 0x7f7c5d00ee10>"
        ]
       },
       {
@@ -786,9 +784,9 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "[ -2.88454926   1.33359582   1.97523353   5.02976188  -0.92369843\n",
-        "  -1.2615812    0.13582678   3.43281277   5.15944677   0.197765\n",
-        "   2.84795422   1.66185525  -0.67031453   1.95990484  10.08883809]\n"
+        "[ 5.21312379  4.07998256 -1.88727855  1.8810597   5.83528749  3.17323975\n",
+        "  3.61095259  6.35085461  0.85512558  7.86604454  1.68210737  4.50882311\n",
+        "  6.25585727  7.3637816   2.37546818]\n"
        ]
       }
      ],
@@ -892,9 +890,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1947b004e0>"
+        "<matplotlib.figure.Figure at 0x7f7c5d0c49b0>"
        ]
       }
      ],
@@ -939,7 +937,7 @@
      "collapsed": false,
      "input": [
       "zs = [sense() for i in range(5000)]\n",
-      "plt.plot(zs)\n",
+      "plt.plot(zs, lw=1)\n",
       "plt.show()"
      ],
      "language": "python",
@@ -948,9 +946,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f19479bbf60>"
+        "<matplotlib.figure.Figure at 0x7f7c5d07d9b0>"
        ]
       }
      ],
@@ -993,7 +991,7 @@
       "    return 10 + rand_student_t(7)*2\n",
       "\n",
       "zs = [sense_t() for i in range(5000)]\n",
-      "plt.plot(zs)\n",
+      "plt.plot(zs, lw=1)\n",
       "plt.show()"
      ],
      "language": "python",
@@ -1002,9 +1000,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAyIAAAGNCAYAAADtmMVMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1950105828>"
+        "<matplotlib.figure.Figure at 0x7f7c5cfb6978>"
        ]
       }
      ],

05_Kalman_Filters.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:45cdaf9dfe065a427fbf85952655b40f5792aae65f05bc5579790995cb4314c0"
+  "signature": "sha256:f266278bc454b99b80665131e586500ae1621e39395bb328b8773e2a40a2afed"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -30,6 +30,7 @@
       "#format the book\n",
       "%matplotlib inline\n",
       "import book_format\n",
+      "from book_format import *\n",
       "book_format.load_style()"
      ],
      "language": "python",
@@ -257,7 +258,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7f4e011cc470>"
+        "<IPython.core.display.HTML at 0x7f86157fe0b8>"
        ]
       }
      ],
@@ -359,10 +360,10 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        " 0.3847\t 2.4259\t-2.2281\t 0.6590\t 0.1281\t\n",
-        " 0.0537\t-0.5158\t-0.6354\t 1.1974\t-0.1386\t\n",
-        "-0.8091\t 0.2634\t 0.0860\t-1.8625\t-0.2827\t\n",
-        "-0.0008\t-0.6972\t-2.3696\t-1.0556\t 0.8438\t\n"
+        " 1.4246\t-0.1778\t 0.6594\t-1.6780\t 0.2510\t\n",
+        "-0.1169\t 1.3496\t-1.8051\t-0.9663\t-0.3046\t\n",
+        " 0.0541\t 1.0872\t 0.6392\t-0.2048\t 0.2456\t\n",
+        " 0.1668\t 0.5042\t-0.8189\t 0.3422\t-2.3804\t\n"
        ]
       }
      ],
@@ -383,6 +384,7 @@
      "input": [
       "import matplotlib.pyplot as plt\n",
       "import matplotlib.pylab as pylab\n",
+      "import book_plots as bp\n",
       "\n",
       "dog = DogSensor(measurement_variance=0.0)\n",
       "xs = []\n",
@@ -390,7 +392,7 @@
       "    x = dog.sense_position()\n",
       "    xs.append(x)\n",
       "    print(\"%.4f\" % x, end=' '),\n",
-      "plt.plot(xs, label='dog position')\n",
+      "bp.plot_track(xs, label='dog position')\n",
       "plt.legend(loc='best')\n",
       "plt.show()"
      ],
@@ -407,9 +409,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f4de831ec88>"
+        "<matplotlib.figure.Figure at 0x7f85fc3e2eb8>"
        ]
       }
      ],
@@ -437,8 +439,8 @@
       "        x = dog.sense_position()\n",
       "        xs.append(x)\n",
       "\n",
-      "    plt.plot(xs, label='sensor')\n",
-      "    plt.plot([0, 99], [1, 100], 'r--', label='ideal')\n",
+      "    bp.plot_track([0, 99], [1, 100])\n",
+      "    bp.plot_measurements(xs, label='Sensor')\n",
       "    plt.xlabel('time')\n",
       "    plt.ylabel('pos')\n",
       "    plt.ylim([0,100])\n",
@@ -446,7 +448,7 @@
       "               measurement_var, process_var))\n",
       "    plt.legend(loc='best')\n",
       "    plt.show()\n",
-      "    \n",
+      "\n",
       "test_sensor(measurement_var=4.0)"
      ],
      "language": "python",
@@ -455,9 +457,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f4de829eb00>"
+        "<matplotlib.figure.Figure at 0x7f85fc106320>"
        ]
       }
      ],
@@ -489,9 +491,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f4de80f5470>"
+        "<matplotlib.figure.Figure at 0x7f85fc3e2518>"
        ]
       }
      ],
@@ -509,9 +511,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f4de823cf28>"
+        "<matplotlib.figure.Figure at 0x7f85fc070898>"
        ]
       }
      ],
@@ -540,9 +542,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f4de81c8c88>"
+        "<matplotlib.figure.Figure at 0x7f85fbfe9588>"
        ]
       }
      ],
@@ -569,9 +571,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f4de817f780>"
+        "<matplotlib.figure.Figure at 0x7f85fbfaa0b8>"
        ]
       }
      ],
@@ -605,9 +607,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f4de80c7e10>"
+        "<matplotlib.figure.Figure at 0x7f85fbecd860>"
        ]
       }
      ],
@@ -635,7 +637,7 @@
       "for i in xs:\n",
       "    ys.append(dog.sense_position())\n",
       "               \n",
-      "plt.plot(xs,ys, label='dog position')\n",
+      "bp.plot_track(xs,ys, label='Dog position')\n",
       "plt.legend(loc='best')\n",
       "plt.show()"
      ],
@@ -645,9 +647,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f4de831edd8>"
+        "<matplotlib.figure.Figure at 0x7f85fc0132e8>"
        ]
       }
      ],
@@ -748,9 +750,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f4de00c7a20>"
+        "<matplotlib.figure.Figure at 0x7f85f26f1160>"
        ]
       }
      ],
@@ -796,9 +798,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f4de00c3438>"
+        "<matplotlib.figure.Figure at 0x7f85f014fac8>"
        ]
       }
      ],
@@ -846,9 +848,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f4de0085080>"
+        "<matplotlib.figure.Figure at 0x7f85f268c748>"
        ]
       }
      ],
@@ -1049,7 +1051,7 @@
      "collapsed": false,
      "input": [
       "def predict(pos, variance, movement, movement_variance):\n",
-      "    return (pos + movement, variance + movement_variance)"
+      "   return (pos + movement, variance + movement_variance)"
      ],
      "language": "python",
      "metadata": {},
@@ -1093,8 +1095,8 @@
       "    print('UPDATE: {: 10.4f} {: 10.4f}'.format(pos[0], pos[1]))\n",
       "\n",
       "    \n",
-      "plt.plot(ps, label='filter')\n",
-      "plt.plot(zs, c='r', linestyle='dashed', label='measurement')\n",
+      "bp.plot_filter(ps)\n",
+      "bp.plot_measurements(zs)\n",
       "plt.legend(loc='best')\n",
       "plt.show()"
      ],
@@ -1120,9 +1122,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f4de00c8240>"
+        "<matplotlib.figure.Figure at 0x7f85f26e4940>"
        ]
       }
      ],
@@ -1241,10 +1243,9 @@
       "    \n",
       "    pos = update(pos[0], pos[1], Z, sensor_variance)\n",
       "    ps.append(pos[0])\n",
-      "    \n",
-      "#plt.subplot(121)    \n",
-      "plt.plot(zs, linestyle='dashed', label='measurement')\n",
-      "plt.plot(ps, label='filter')\n",
+      "       \n",
+      "bp.plot_measurements(zs)\n",
+      "bp.plot_filter(ps)\n",
       "plt.legend(loc=2)\n",
       "plt.show()\n",
       "\n",
@@ -1261,17 +1262,17 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f4de0103ac8>"
+        "<matplotlib.figure.Figure at 0x7f85f2739160>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f4de0041cf8>"
+        "<matplotlib.figure.Figure at 0x7f85fc020d68>"
        ]
       },
       {
@@ -1393,7 +1394,8 @@
       "        self.P = P\n",
       "        self.R = R\n",
       "        self.Q = Q\n",
-      "        \n",
+      "\n",
+      "\n",
       "    def update(self, z):\n",
       "        self.x = (self.P * z + self.x * self.R) / (self.P + self.R)\n",
       "        self.P = 1. / (1./self.P + 1./self.R)\n",
@@ -1581,9 +1583,9 @@
       "    ps.append(kf.P)\n",
       "\n",
       "# plot the filter output and the variance\n",
-      "plt.scatter(range(N), zs, marker='+', s=64, color='r', label='measurements')\n",
-      "p1, = plt.plot(estimates, label='filter')\n",
-      "plt.legend(loc='best', scatterpoints=1)\n",
+      "bp.plot_measurements(zs)\n",
+      "bp.plot_filter(estimates)\n",
+      "plt.legend(loc='best')\n",
       "plt.xlim((0,N));plt.ylim((0,30))\n",
       "plt.show()\n",
       "plt.plot(ps)\n",
@@ -1597,17 +1599,17 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f4de81c83c8>"
+        "<matplotlib.figure.Figure at 0x7f85fc3e2c88>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f4dd80c54e0>"
+        "<matplotlib.figure.Figure at 0x7f85f8033e48>"
        ]
       },
       {
@@ -1725,9 +1727,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f4de817f550>"
+        "<matplotlib.figure.Figure at 0x7f85f0149a58>"
        ]
       }
      ],
@@ -1791,8 +1793,8 @@
       "\n",
       "    pos = predict(pos[0], pos[1], movement+ random.randn(), movement_variance)\n",
       "\n",
-      "plt.plot(ps, label='filter')\n",
-      "plt.plot(zs, linestyle='dashed', c='r', label='measurement')\n",
+      "bp.plot_filter(ps)\n",
+      "bp.plot_measurements(zs)\n",
       "plt.legend()\n",
       "plt.show()\n",
       "plt.plot(vs)\n",
@@ -1805,17 +1807,17 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f4de815a588>"
+        "<matplotlib.figure.Figure at 0x7f85f26ee278>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f4de8229b70>"
+        "<matplotlib.figure.Figure at 0x7f85f003b0f0>"
        ]
       }
      ],
@@ -1880,8 +1882,8 @@
       "    ps.append(pos[0])\n",
       "\n",
       "\n",
-      "p1, = plt.plot(zs, c='r', linestyle='dashed', label='measurement')\n",
-      "p2, = plt.plot(ps, c='#004080', label='filter')\n",
+      "bp.plot_measurements(zs, lw=1)\n",
+      "bp.plot_filter(ps)\n",
       "plt.legend(loc='best')\n",
       "plt.show()"
      ],
@@ -1891,9 +1893,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f4de821ce80>"
+        "<matplotlib.figure.Figure at 0x7f85f268c7f0>"
        ]
       }
      ],
@@ -1944,9 +1946,10 @@
       "    pos = update(pos[0], pos[1], Z, sensor_variance)\n",
       "    ps.append(pos[0])\n",
       "\n",
-      "plt.plot(ps, label='filter')\n",
-      "plt.plot(zs, c='r', linestyle='dashed', label='measurement')\n",
+      "bp.plot_filter(ps)\n",
+      "bp.plot_measurements(zs)\n",
       "plt.legend(loc='best')\n",
+      "plt.gca().set_xlim(0,100)\n",
       "plt.show()"
      ],
      "language": "python",
@@ -1955,9 +1958,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f4dd2faa358>"
+        "<matplotlib.figure.Figure at 0x7f85eabceef0>"
        ]
       }
      ],
@@ -2007,8 +2010,8 @@
       "    pos = update(pos[0], pos[1], Z, sensor_variance)\n",
       "    ps.append(pos[0])\n",
       "\n",
-      "plt.plot(zs, c='r', linestyle='dashed', label='measurement')\n",
-      "plt.plot(ps, c='#004080', label='filter')\n",
+      "bp.plot_measurements(zs, lw=1)\n",
+      "bp.plot_filter(ps)\n",
       "plt.legend(loc='best')\n",
       "plt.show()"
      ],
@@ -2018,9 +2021,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f4de0140710>"
+        "<matplotlib.figure.Figure at 0x7f85eabce550>"
        ]
       }
      ],
@@ -2059,8 +2062,8 @@
       "\n",
       "    pos = predict(pos[0], pos[1], movement, movement_variance)\n",
       "\n",
-      "plt.plot(zs, c='r', linestyle='dashed', label='measurement')\n",
-      "plt.plot(ps, c='#004080', label='filter')\n",
+      "bp.plot_measurements(zs, lw=1)\n",
+      "bp.plot_filter(ps)\n",
       "plt.legend(loc='best')\n",
       "plt.show()"
      ],
@@ -2070,9 +2073,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f4dd2ff5cc0>"
+        "<matplotlib.figure.Figure at 0x7f85f26fc978>"
        ]
       }
      ],
@@ -2199,9 +2202,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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        "text": [
-        "<matplotlib.figure.Figure at 0x7f4de80f5e10>"
+        "<matplotlib.figure.Figure at 0x7f85fc020780>"
        ]
       },
       {
@@ -2289,9 +2292,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f4de82275c0>"
+        "<matplotlib.figure.Figure at 0x7f85f26fb8d0>"
        ]
       }
      ],
@@ -2374,9 +2377,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f4de003d400>"
+        "<matplotlib.figure.Figure at 0x7f85f2702a90>"
        ]
       }
      ],

06_Multivariate_Kalman_Filters.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:e6b03302db96abb18b25b58b48ed64177e88c5da12333f9bf9dadc3fd9843de7"
+  "signature": "sha256:b0ce987c1b945a8915aadb61595c971e98cd0377ac4a1dba9a1b8bcd787f3736"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -259,7 +259,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7fdf3a08e9e8>"
+        "<IPython.core.display.HTML at 0x7ff905d616a0>"
        ]
       }
      ],
@@ -391,9 +391,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fdf214d0b00>"
+        "<matplotlib.figure.Figure at 0x7ff8ed885a90>"
        ]
       }
      ],
@@ -559,9 +559,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fdf3a0939b0>"
+        "<matplotlib.figure.Figure at 0x7ff905d64588>"
        ]
       }
      ],
@@ -593,9 +593,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fdf3a0937b8>"
+        "<matplotlib.figure.Figure at 0x7ff905d64860>"
        ]
       }
      ],
@@ -651,9 +651,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fdf200e9c88>"
+        "<matplotlib.figure.Figure at 0x7ff8e1be2b38>"
        ]
       }
      ],
@@ -754,9 +754,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fdf1533e400>"
+        "<matplotlib.figure.Figure at 0x7ff8e18dc710>"
        ]
       }
      ],
@@ -781,9 +781,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7fdf153ea198>"
+        "<matplotlib.figure.Figure at 0x7ff8e1a87358>"
        ]
       }
      ],
@@ -808,9 +808,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fdf151469e8>"
+        "<matplotlib.figure.Figure at 0x7ff8e17de6a0>"
        ]
       }
      ],
@@ -839,9 +839,9 @@
       {
        "metadata": {},
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        "text": [
-        "<matplotlib.figure.Figure at 0x7fdf152479b0>"
+        "<matplotlib.figure.Figure at 0x7ff8e199c780>"
        ]
       }
      ],
@@ -904,9 +904,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fdf14fbb240>"
+        "<matplotlib.figure.Figure at 0x7ff8e19a2cf8>"
        ]
       }
      ],
@@ -1993,27 +1993,39 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
+      "import book_plots as bp\n",
+      "\n",
       "def plot_track(noise_var=None, count=0, R_var=0, Q_var=0, P=500., \n",
-      "               initial_x=None,data=None, plot_P=True, \n",
+      "               initial_x=None,data=None, plot_P=True, y_lim=None,\n",
       "               title='Kalman Filter'):\n",
       "    \n",
       "    ps, zs, cov = filter_dog(noise_var=noise_var, data=data, count=count,\n",
       "                             R=R_var, Q=Q_var, P=P, initial_x=initial_x)\n",
       "    \n",
-      "    actual = np.array(list(range(0,count)))\n",
+      "    actual = np.linspace(0, count-1, count)\n",
       "    cov = np.asarray(cov)\n",
-      "    std3 = np.sqrt(cov[:,0,0])*1\n",
-      "    std3_top = np.minimum(actual+std3, [count+10])\n",
-      "    std3_btm = np.maximum(actual-std3, [-50])\n",
-      "    plt.plot(actual, label='actual')\n",
-      "    plt.plot(range(1, count+1), zs, linestyle='dashed', label='measurement')\n",
-      "    plt.plot(range(1, count+1), ps, label='filter')\n",
-      "    plt.plot(range(count), actual+std3, linestyle='--', c='k')\n",
-      "    plt.plot(range(count), actual-std3, linestyle='--', c='k')\n",
-      "    plt.fill_between(range(count), std3_btm, std3_top,\n",
+      "    std = np.sqrt(cov[:,0,0])\n",
+      "    std_top = np.minimum(actual+std, [count+10])\n",
+      "    std_btm = np.maximum(actual-std, [-50])\n",
+      "\n",
+      "    std_top = actual+std\n",
+      "    std_btm = actual-std\n",
+      "    \n",
+      "    bp.plot_track(actual)\n",
+      "    bp.plot_measurements(range(1, count+1), zs)\n",
+      "    bp.plot_filter(range(1, count+1), ps)\n",
+      "\n",
+      "    plt.plot(actual+std, linestyle=':', c='k', lw=2)\n",
+      "    plt.plot(actual-std, linestyle=':', c='k', lw=2)\n",
+      "    plt.fill_between(range(count), std_btm, std_top,\n",
       "                     facecolor='yellow', alpha=0.2)\n",
       "    plt.legend(loc=4)\n",
-      "    plt.ylim((-50, count+10))\n",
+      "    if y_lim is not None:\n",
+      "        plt.ylim(y_lim)\n",
+      "    else:\n",
+      "        plt.ylim((-50, count+10))\n",
+      "        \n",
+      "    plt.xlim((0,count))\n",
       "    plt.title(title)\n",
       "    plt.show()\n",
       "    \n",
@@ -2048,7 +2060,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "plot_track(noise_var=10, R_var=10., Q_var=0.01, count=50)"
+      "plot_track(noise_var=10, R_var=10., Q_var=0.01, count=50, y_lim=(-20,50))"
      ],
      "language": "python",
      "metadata": {},
@@ -2056,17 +2068,17 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fdf1515ef98>"
+        "<matplotlib.figure.Figure at 0x7ff8e1bd95c0>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fdf15131a58>"
+        "<matplotlib.figure.Figure at 0x7ff8e1b5dba8>"
        ]
       }
      ],
@@ -2233,17 +2245,17 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fdf152d57b8>"
+        "<matplotlib.figure.Figure at 0x7ff8e164acf8>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fdf14fb87f0>"
+        "<matplotlib.figure.Figure at 0x7ff8e1b6e828>"
        ]
       }
      ],
@@ -2477,9 +2489,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fdf2011ccc0>"
+        "<matplotlib.figure.Figure at 0x7ff8e1b83978>"
        ]
       }
      ],
@@ -2547,17 +2559,17 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fdf14fd1e80>"
+        "<matplotlib.figure.Figure at 0x7ff8e1aecac8>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fdf14fcbf28>"
+        "<matplotlib.figure.Figure at 0x7ff8e183cda0>"
        ]
       }
      ],
@@ -2598,9 +2610,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fdf152ebc88>"
+        "<matplotlib.figure.Figure at 0x7ff8e1637d68>"
        ]
       }
      ],
@@ -2627,9 +2639,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7fdf1520e710>"
+        "<matplotlib.figure.Figure at 0x7ff8e1c0da20>"
        ]
       }
      ],
@@ -2656,9 +2668,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fdf2009efd0>"
+        "<matplotlib.figure.Figure at 0x7ff8e1883ef0>"
        ]
       }
      ],
@@ -2725,9 +2737,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fdf151c4518>"
+        "<matplotlib.figure.Figure at 0x7ff8e1aa2710>"
        ]
       }
      ],
@@ -2755,9 +2767,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fdf2012ff28>"
+        "<matplotlib.figure.Figure at 0x7ff8e1a17d30>"
        ]
       }
      ],
@@ -2827,9 +2839,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fdf1536d630>"
+        "<matplotlib.figure.Figure at 0x7ff8e194ec50>"
        ]
       }
      ],
@@ -2849,7 +2861,7 @@
      "collapsed": false,
      "input": [
       "plot_track(data=zs, R_var=50., Q_var=.2, P=1., count=len(zs), \n",
-      "           plot_P=False, title='P=1 m')"
+      "           y_lim=(-20,65), plot_P=False, title='P=1 m')"
      ],
      "language": "python",
      "metadata": {},
@@ -2857,9 +2869,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7fdf15250080>"
+        "<matplotlib.figure.Figure at 0x7ff8e181c2e8>"
        ]
       }
      ],
@@ -2890,9 +2902,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7fdf20077860>"
+        "<matplotlib.figure.Figure at 0x7ff8e180e0b8>"
        ]
       }
      ],
@@ -2921,9 +2933,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fdf14fe3a90>"
+        "<matplotlib.figure.Figure at 0x7ff8e1b1b358>"
        ]
       }
      ],
@@ -2965,9 +2977,8 @@
       "        zs.append(z)\n",
       "        f.predict()\n",
       "\n",
-      "    plt.plot([0, count], [0, count], 'g', label='actual')\n",
-      "    plt.plot(range(1,count+1), zs, c='r', linestyle='dashed', \n",
-      "             label='measurement')\n",
+      "    bp.plot_track([0, count], [0, count])\n",
+      "    bp.plot_measurements(range(1,count+1), zs)\n",
       "    plt.plot(range(1,count+1), ps, c='b', label='filter')\n",
       "    plt.legend(loc='best')\n",
       "    plt.title(title)\n",
@@ -2975,13 +2986,14 @@
       "    for i,p in enumerate(cov):\n",
       "        stats.plot_covariance_ellipse(\n",
       "              (i+1, ps[i]), cov=p, axis_equal=False, \n",
-      "               facecolor='g', edgecolor=None, alpha=0.2)\n",
+      "               facecolor='g', edgecolor=None, alpha=0.6)\n",
       "\n",
       "        if i == len(cov)-1:\n",
       "            s = ('$\\sigma^2_{pos} = %.2f$' % p[0,0])\n",
       "            plt.text (20, 10, s, fontsize=18)\n",
       "            s = ('$\\sigma^2_{vel} = %.2f$' % p[1,1])\n",
       "            plt.text (20, 5, s, fontsize=18)\n",
+      "    plt.axis('equal')\n",
       "    plt.show()\n",
       "\n",
       "\n",
@@ -2994,17 +3006,17 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fdf2012fd68>"
+        "<matplotlib.figure.Figure at 0x7ff8e1a9a208>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fdf1526bd68>"
+        "<matplotlib.figure.Figure at 0x7ff8e18b1f28>"
        ]
       }
      ],
@@ -3077,9 +3089,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fdf152eb160>"
+        "<matplotlib.figure.Figure at 0x7ff8e1a9cb70>"
        ]
       }
      ],
@@ -3144,10 +3156,9 @@
       "zs = [dog.sense() for t in range(count)]\n",
       "Xs, _, _, _ = f.batch_filter(zs)\n",
       "\n",
-      "plt.plot([0, count], [0, count], 'g', label='actual')\n",
-      "plt.plot(range(1,count+1), zs, c='r', linestyle='dashed', \n",
-      "         label='measurement')\n",
-      "plt.plot(range(1,count+1), Xs[:,0], c='b', label='filter')\n",
+      "bp.plot_track([0, count], [0, count])\n",
+      "bp.plot_measurements(range(1,count+1), zs)\n",
+      "bp.plot_filter(range(1,count+1), Xs[:,0])\n",
       "plt.legend(loc='best')\n",
       "plt.show()"
      ],
@@ -3157,9 +3168,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fdf151116a0>"
+        "<matplotlib.figure.Figure at 0x7ff8e175cf98>"
        ]
       }
      ],
@@ -3201,11 +3212,9 @@
       "Xs, Covs, _, _ = f.batch_filter(zs)\n",
       "Ms, Ps, _ = f.rts_smoother(Xs, Covs)\n",
       "\n",
-      "#plt.plot([0, count], [0, count], 'g', label='actual', alpha=.7)\n",
-      "plt.plot(range(1,count+1), zs, c='r', linestyle='dashed', \n",
-      "         alpha=.5, label='measurement')\n",
-      "plt.plot(range(1,count+1), Xs[:,0], c='b', label='filter', alpha=.5)\n",
-      "plt.plot(range(1,count+1), Ms[:,0], c='k', label='RTS')\n",
+      "bp.plot_measurements(range(1,count+1), zs)\n",
+      "bp.plot_filter(range(1,count+1), Xs[:,0])\n",
+      "plt.plot(range(1,count+1), Ms[:,0], label='RTS')\n",
       "plt.legend(loc='best')\n",
       "plt.show()"
      ],
@@ -3215,9 +3224,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fdf151115c0>"
+        "<matplotlib.figure.Figure at 0x7ff8e199aa58>"
        ]
       }
      ],
@@ -3247,8 +3256,8 @@
       "zs = [dog.sense() for t in range(count)]\n",
       "Xs, Covs, _, _ = f.batch_filter(zs)\n",
       "Ms, Ps, _ = f.rts_smoother(Xs, Covs)\n",
-      "plt.plot(Xs[:,1], c='b', label='filter', alpha=.5)\n",
-      "plt.plot(Ms[:,1], c='k', linestyle=':', label='RTS')\n",
+      "bp.plot_filter(Xs[:,1])\n",
+      "plt.plot(Ms[:,1], label='RTS')\n",
       "plt.legend(loc='best')\n",
       "plt.gca().axhline(1, c='k')\n",
       "plt.show()"
@@ -3259,9 +3268,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fdf152e6f98>"
+        "<matplotlib.figure.Figure at 0x7ff8e199ae80>"
        ]
       }
      ],
@@ -3281,9 +3290,9 @@
      "collapsed": false,
      "input": [
       "dx = np.diff(zs, axis=0)\n",
-      "plt.plot(range(1,count), dx, c='r',ls='--', label='raw velocity')\n",
-      "plt.plot(Xs[:,1], c='b', label='filter', alpha=.5)\n",
-      "plt.plot(Ms[:,1], c='k', linestyle=':', label='RTS')\n",
+      "plt.plot(Xs[:,1], label='Filter')\n",
+      "plt.plot(Ms[:,1], label='RTS')\n",
+      "plt.plot(range(1,count), dx, c='r',ls='--', lw=2, label='Raw velocity')\n",
       "plt.legend(loc='best')\n",
       "plt.show()"
      ],
@@ -3293,9 +3302,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fdf15111630>"
+        "<matplotlib.figure.Figure at 0x7ff8e1747978>"
        ]
       }
      ],

07_Kalman_Filter_Math.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:5f83a9ad789c1e39b3baf7784f5a29653a668d597037e30e671943b708366e53"
+  "signature": "sha256:ed0b405c8ffb065407f38ce863d85b30b6b2bc8baad8971131f305b6e547b206"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -259,7 +259,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7f842d6f86a0>"
+        "<IPython.core.display.HTML at 0x7f51f20896a0>"
        ]
       }
      ],
@@ -735,9 +735,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f0a48014898>"
+        "<matplotlib.figure.Figure at 0x7f51f208c5f8>"
        ]
       }
      ],
@@ -1457,7 +1457,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 15
+     "prompt_number": 14
     },
     {
      "cell_type": "markdown",
@@ -1477,6 +1477,7 @@
      "collapsed": false,
      "input": [
       "import math\n",
+      "import numpy as np\n",
       "t = 0.\n",
       "y = 1.\n",
       "dt = .1\n",
@@ -1500,7 +1501,7 @@
       "plt.show()\n",
       "\n",
       "error = np.array(exact) - np.array(ys)\n",
-      "print(\"max error {})"
+      "print(\"max error {}\".format(max(error)))"
      ],
      "language": "python",
      "metadata": {},
@@ -1508,13 +1509,20 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f0a3ce895f8>"
+        "<matplotlib.figure.Figure at 0x7f51ccf45ef0>"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "max error 5.206970035942504e-05\n"
        ]
       }
      ],
-     "prompt_number": 27
+     "prompt_number": 15
     },
     {
      "cell_type": "heading",

09_Extended_Kalman_Filters.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:0d44e081a2149e5b4781c1b684f7e6682b1af05cf278c8fc3e56745828aa6b87"
+  "signature": "sha256:b56d774f67e3e3e8ef73a277a8c8fc0385cd9913dcf27fa00ec455fa653f326c"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -259,7 +259,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7f80f279d6a0>"
+        "<IPython.core.display.HTML at 0x7fe744ebb6a0>"
        ]
       }
      ],
@@ -356,9 +356,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f80e1bf0cf8>"
+        "<matplotlib.figure.Figure at 0x7fe7289fcb70>"
        ]
       }
      ],
@@ -388,9 +388,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f80e19361d0>"
+        "<matplotlib.figure.Figure at 0x7fe7289fcba8>"
        ]
       }
      ],
@@ -422,9 +422,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f80e1bf0b00>"
+        "<matplotlib.figure.Figure at 0x7fe7282874e0>"
        ]
       }
      ],
@@ -458,9 +458,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f80e151e240>"
+        "<matplotlib.figure.Figure at 0x7fe741e39198>"
        ]
       }
      ],
@@ -487,8 +487,8 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "input  mean, variance: -0.0003, 0.9968\n",
-        "output mean, variance: -0.0272, 2.2340\n"
+        "input  mean, variance: -0.0023, 1.0018\n",
+        "output mean, variance: -0.0242, 2.2448\n"
        ]
       }
      ],
@@ -517,16 +517,16 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f80e12d96d8>"
+        "<matplotlib.figure.Figure at 0x7fe7281e37f0>"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "output mean, variance: 0.0005, 2.2428\n"
+        "output mean, variance: 0.0034, 2.2540\n"
        ]
       }
      ],
@@ -561,8 +561,8 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "linear    output mean, variance: 0.0267, 7457.7205\n",
-        "nonlinear output mean, variance: -1.8258, 26091.4544\n"
+        "linear    output mean, variance: 0.1959, 7495.1963\n",
+        "nonlinear output mean, variance: -1.4855, 26268.5084\n"
        ]
       }
      ],
@@ -609,9 +609,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f80e142f0f0>"
+        "<matplotlib.figure.Figure at 0x7fe727220ef0>"
        ]
       }
      ],
@@ -632,8 +632,8 @@
      "input": [
       "def y(x): \n",
       "    return 8*x - 12.75\n",
-      "plt.plot (xs, ys,c='k')\n",
-      "plt.plot ([1.25, 1.75], [y(1.25), y(1.75)], c='r')\n",
+      "plt.plot (xs, ys)\n",
+      "plt.plot ([1.25, 1.75], [y(1.25), y(1.75)])\n",
       "plt.xlim(1,2)\n",
       "plt.ylim([-1.5, 1])\n",
       "plt.show()"
@@ -644,9 +644,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f80e14b4ef0>"
+        "<matplotlib.figure.Figure at 0x7fe7283370b8>"
        ]
       }
      ],
@@ -673,8 +673,8 @@
       "def y(x): \n",
       "    return x - 2.25\n",
       "\n",
-      "plt.plot (xs, ys,c='k')\n",
-      "plt.plot ([1,2], [y(1),y(2)], c='r')\n",
+      "plt.plot(xs, ys)\n",
+      "plt.plot([1,2], [y(1),y(2)])\n",
       "plt.xlim(1,2)\n",
       "plt.ylim([-1.5, 1])\n",
       "plt.show()"
@@ -685,9 +685,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f80e13f2cc0>"
+        "<matplotlib.figure.Figure at 0x7fe7271f0a20>"
        ]
       }
      ],
@@ -758,7 +758,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f80e276b2e8>"
+        "<matplotlib.figure.Figure at 0x7fe72833a9b0>"
        ]
       }
      ],
@@ -1068,6 +1068,7 @@
      "input": [
       "from filterpy.kalman import ExtendedKalmanFilter\n",
       "from numpy import eye, array, asarray\n",
+      "import book_plots as bp\n",
       "\n",
       "dt = 0.05\n",
       "rk = ExtendedKalmanFilter(dim_x=3, dim_z=1)\n",
@@ -1110,23 +1111,23 @@
       "time = np.arange(0,len(xs)*dt, dt)\n",
       "\n",
       "plt.figure()\n",
-      "plt.plot(time, track[:,0], label='track')\n",
-      "plt.plot(time, xs[:,0], label='filter')\n",
+      "bp.plot_track(time, track[:,0])\n",
+      "bp.plot_filter(time, xs[:,0])\n",
       "plt.legend(loc=4)\n",
       "plt.xlabel('time (sec)')\n",
       "plt.ylabel('position (m)')\n",
       "\n",
       "\n",
       "plt.figure()\n",
-      "plt.plot(time, track[:,1], label='track')\n",
-      "plt.plot(time, xs[:,1])\n",
+      "bp.plot_track(time, track[:,1])\n",
+      "bp.plot_filter(time, xs[:,1])\n",
       "plt.legend(loc=4)\n",
       "plt.xlabel('time (sec)')\n",
       "plt.ylabel('velocity (m/s)')\n",
       "\n",
       "plt.figure()\n",
-      "plt.plot(time, track[:,2], label='track')\n",
-      "plt.plot(time, xs[:,2])\n",
+      "bp.plot_track(time, track[:,2])\n",
+      "bp.plot_filter(time, xs[:,2])\n",
       "plt.ylabel('altitude (m)')\n",
       "plt.legend(loc=4)\n",
       "plt.xlabel('time (sec)')\n",
@@ -1139,25 +1140,25 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f80f27a27b8>"
+        "<matplotlib.figure.Figure at 0x7fe7283624e0>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f80e035bf60>"
+        "<matplotlib.figure.Figure at 0x7fe7270f4470>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f80e032cba8>"
+        "<matplotlib.figure.Figure at 0x7fe727a2a198>"
        ]
       }
      ],
@@ -1334,9 +1335,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f80e14c76d8>"
+        "<matplotlib.figure.Figure at 0x7fe7282a84a8>"
        ]
       }
      ],

10_Unscented_Kalman_Filter.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:25a07fbbd0e9ae161c0147f461a1bdd2aeaf6a8adb9675a094737edd26ff3aff"
+  "signature": "sha256:07d552b7c574d0e22b2d46189f6004982b2ea187162610c14413b153f2941e87"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -259,7 +259,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7fcf977a16a0>"
+        "<IPython.core.display.HTML at 0x7f07935586a0>"
        ]
       }
      ],
@@ -307,9 +307,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fcf7a917128>"
+        "<matplotlib.figure.Figure at 0x7f07770b3710>"
        ]
       }
      ],
@@ -345,9 +345,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fcf7a90e518>"
+        "<matplotlib.figure.Figure at 0x7f0777090518>"
        ]
       }
      ],
@@ -394,7 +394,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fcf7abfea90>"
+        "<matplotlib.figure.Figure at 0x7f077707be10>"
        ]
       }
      ],
@@ -512,16 +512,16 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fcf7a8b9f98>"
+        "<matplotlib.figure.Figure at 0x7f0776b8b4a8>"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "Difference in mean x=-0.037, y=43.337\n"
+        "Difference in mean x=-0.002, y=43.007\n"
        ]
       }
      ],
@@ -571,9 +571,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7fcf7a80f240>"
+        "<matplotlib.figure.Figure at 0x7f0776de6278>"
        ]
       }
      ],
@@ -613,7 +613,7 @@
        "output_type": "display_data",
        "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7fcf711a0d68>"
+        "<matplotlib.figure.Figure at 0x7f076d3e0ef0>"
        ]
       }
      ],
@@ -666,9 +666,9 @@
       {
        "metadata": {},
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fcf7112a080>"
+        "<matplotlib.figure.Figure at 0x7f076d3c7438>"
        ]
       }
      ],
@@ -1079,9 +1079,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fcf729e1cc0>"
+        "<matplotlib.figure.Figure at 0x7f07904d75f8>"
        ]
       }
      ],
@@ -1111,9 +1111,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fcf7110dfd0>"
+        "<matplotlib.figure.Figure at 0x7f076d429a20>"
        ]
       }
      ],
@@ -1157,9 +1157,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fcf7118d630>"
+        "<matplotlib.figure.Figure at 0x7f0776b99278>"
        ]
       }
      ],
@@ -1280,9 +1280,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fcf7117e160>"
+        "<matplotlib.figure.Figure at 0x7f0772610cc0>"
        ]
       }
      ],
@@ -1312,9 +1312,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fcf72a3c7f0>"
+        "<matplotlib.figure.Figure at 0x7f076d42f7b8>"
        ]
       }
      ],
@@ -1412,9 +1412,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fcf72a369b0>"
+        "<matplotlib.figure.Figure at 0x7f0776b682b0>"
        ]
       }
      ],
@@ -1463,9 +1463,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fcf710e8320>"
+        "<matplotlib.figure.Figure at 0x7f076d385470>"
        ]
       }
      ],
@@ -1538,9 +1538,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fcf7116b2b0>"
+        "<matplotlib.figure.Figure at 0x7f076d325d30>"
        ]
       }
      ],
@@ -1659,9 +1659,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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+       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7fcf72a46780>"
+        "<matplotlib.figure.Figure at 0x7f076d3964a8>"
        ]
       }
      ],
@@ -1730,9 +1730,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fcf71143630>"
+        "<matplotlib.figure.Figure at 0x7f0776de6518>"
        ]
       }
      ],
@@ -1830,9 +1830,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fcf7a782080>"
+        "<matplotlib.figure.Figure at 0x7f076d429d68>"
        ]
       }
      ],
@@ -1865,9 +1865,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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+       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7fcf72a7a908>"
+        "<matplotlib.figure.Figure at 0x7f076d41f518>"
        ]
       }
      ],
@@ -1977,9 +1977,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fcf72a36ac8>"
+        "<matplotlib.figure.Figure at 0x7f07725400f0>"
        ]
       },
       {
@@ -2751,7 +2751,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fcf7a6a18d0>"
+        "<matplotlib.figure.Figure at 0x7f0776b2a0b8>"
        ]
       }
      ],
@@ -2867,9 +2867,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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        "text": [
-        "<matplotlib.figure.Figure at 0x7fcf729f1780>"
+        "<matplotlib.figure.Figure at 0x7f076d419908>"
        ]
       }
      ],
@@ -2977,9 +2977,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fcf72a36358>"
+        "<matplotlib.figure.Figure at 0x7f076d2e1978>"
        ]
       }
      ],
@@ -3017,22 +3017,25 @@
       "Ms,_,_ = kf.rts_smoother(xs, covs)\n",
       "plt.figure()\n",
       "plt.subplot(311)\n",
-      "plt.plot(t[0:100], xs[0:100,0])\n",
-      "plt.plot(t[0:100], Ms[0:100,0],c='k')\n",
+      "plt.plot(t[0:100], xs[0:100,0], label='KF')\n",
+      "plt.plot(t[0:100], Ms[0:100,0], c='k', label='RTS')\n",
       "plt.xlabel('time(sec)')\n",
       "plt.ylabel('position')\n",
+      "plt.legend(loc=4)\n",
       "\n",
       "plt.subplot(312)\n",
-      "plt.plot(t[0:100], xs[0:100,1])\n",
-      "plt.plot(t[0:100], Ms[0:100,1],c='k')\n",
+      "plt.plot(t[0:100], xs[0:100,1], label='KF')\n",
+      "plt.plot(t[0:100], Ms[0:100,1], c='k', label='RTS')\n",
       "plt.xlabel('time(sec)')\n",
       "plt.ylabel('velocity')\n",
+      "plt.legend(loc=4)\n",
       "\n",
       "plt.subplot(313)\n",
-      "plt.plot(t[0:100], xs[0:100,2])\n",
-      "plt.plot(t[0:100], Ms[0:100,2],c='k')\n",
+      "plt.plot(t[0:100], xs[0:100,2], label='KF')\n",
+      "plt.plot(t[0:100], Ms[0:100,2], c='k', label='RTS')\n",
       "plt.xlabel('time(sec)')\n",
       "plt.ylabel('altitude')\n",
+      "plt.legend(loc=1)\n",
       "plt.show()"
      ],
      "language": "python",
@@ -3041,13 +3044,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fcf7a6a2320>"
+        "<matplotlib.figure.Figure at 0x7f07725ff940>"
        ]
       }
      ],
-     "prompt_number": 39
+     "prompt_number": 41
     },
     {
      "cell_type": "markdown",

11_Ensemble_Kalman_Filters.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:a404121cbe8fa33c5a1840055688ac0af387004638bb55fdf856d15a24a90c2d"
+  "signature": "sha256:fb409161026d1ed0dd77233253724a3d5243888476f399fe92241d7cbe8f6d56"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -259,7 +259,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7f0170b389e8>"
+        "<IPython.core.display.HTML at 0x7f143cb0f6a0>"
        ]
       }
      ],
@@ -310,9 +310,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f01536eb4a8>"
+        "<matplotlib.figure.Figure at 0x7f1430666c50>"
        ]
       }
      ],
@@ -561,7 +561,7 @@
       "from filterpy.kalman import EnsembleKalmanFilter as EnKF\n",
       "from filterpy.kalman import KalmanFilter\n",
       "from filterpy.common import Q_discrete_white_noise\n",
-      "\n",
+      "import book_plots as bp\n",
       "\n",
       "np.random.seed(1234)\n",
       "\n",
@@ -616,11 +616,11 @@
       "ps = np.asarray(ps)\n",
       "\n",
       "plt.plot(results, label='EnKF')\n",
-      "plt.plot(kf_results, label='KF', c='g')\n",
-      "plt.plot(measurements, c='r', label='z')\n",
-      "plt.plot (results-ps, c='k',linestyle='--', label='3$\\sigma$')\n",
-      "plt.plot(results+ps, c='k', linestyle='--')\n",
-      "plt.fill_between(range(100),results-ps, results+ps, facecolor='yellow', alpha=.3)\n",
+      "plt.plot(kf_results, label='KF', c='b', lw=2)\n",
+      "bp.plot_measurements(measurements)\n",
+      "plt.plot (results-ps, c='k',linestyle=':', lw=1, label='1$\\sigma$')\n",
+      "plt.plot(results+ps, c='k', linestyle=':', lw=1)\n",
+      "plt.fill_between(range(100),results-ps, results+ps, facecolor='y', alpha=.3)\n",
       "plt.legend(loc='best')\n",
       "plt.show()"
      ],
@@ -630,13 +630,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f014c4f1cf8>"
+        "<matplotlib.figure.Figure at 0x7f14189316d8>"
        ]
       }
      ],
-     "prompt_number": 3
+     "prompt_number": 5
     },
     {
      "cell_type": "markdown",

12_Designing_Nonlinear_Kalman_Filters.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:c881a294028594ad9b0c22639019307db6edb57f4ccf0d8621859f194972fd02"
+  "signature": "sha256:cd2405175a06c2ebdde08ed8610f15c145ad09340c50cd4c69b54309d12deb44"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -259,7 +259,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7f470e8c4630>"
+        "<IPython.core.display.HTML at 0x7f04335466a0>"
        ]
       }
      ],
@@ -402,9 +402,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f470e8c9668>"
+        "<matplotlib.figure.Figure at 0x7f0417074c50>"
        ]
       }
      ],
@@ -759,6 +759,7 @@
       "import math\n",
       "from filterpy.kalman import KalmanFilter\n",
       "import numpy as np\n",
+      "import book_plots as bp\n",
       "\n",
       "pos_a = (100, -20)\n",
       "pos_b = (-100, -20)\n",
@@ -812,8 +813,8 @@
       "    f1.update(z)\n",
       "\n",
       "\n",
-      "plt.plot(xs, ys, 'r--', label='filter')\n",
-      "plt.plot(pxs, pys, label='ideal')\n",
+      "bp.plot_filter(xs, ys)\n",
+      "bp.plot_track(pxs, pys)\n",
       "plt.legend(loc=2)\n",
       "plt.show()"
      ],
@@ -823,9 +824,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f46f4a8a438>"
+        "<matplotlib.figure.Figure at 0x7f0415d776a0>"
        ]
       }
      ],

13_HInfinity_Filters.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:78c13872e6c2cb52bc6db77a5e5449ba5b096adea22234e39ae1fdcb9adbea78"
+  "signature": "sha256:3902df81d4a65468d74c91a7d5441eba58bde48d630e4949c25bd244969debdb"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -257,7 +257,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7f745f93eb00>"
+        "<IPython.core.display.HTML at 0x7f781e4548d0>"
        ]
       }
      ],
@@ -330,9 +330,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f7442adbc18>"
+        "<matplotlib.figure.Figure at 0x7f7801fc2278>"
        ]
       }
      ],

14_Smoothing.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:c0caab3016d3aa22ec9277ab90ab046e1fe26bbbe4c1423ed8ba2c60cb49bd57"
+  "signature": "sha256:e79511f22a0b5746eaba827fe690183edf91655838106bfbbdbae77f6a72900e"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -259,7 +259,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7f36d98c06a0>"
+        "<IPython.core.display.HTML at 0x7f63c05296a0>"
        ]
       }
      ],
@@ -296,9 +296,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f36bcd34b38>"
+        "<matplotlib.figure.Figure at 0x7f63a4064e10>"
        ]
       }
      ],
@@ -337,9 +337,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f36bca77160>"
+        "<matplotlib.figure.Figure at 0x7f63a3d97080>"
        ]
       }
      ],
@@ -373,9 +373,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f36bc9daf98>"
+        "<matplotlib.figure.Figure at 0x7f63a3ce79b0>"
        ]
       }
      ],
@@ -515,6 +515,7 @@
       "from numpy import random\n",
       "import matplotlib.pyplot as plt\n",
       "from filterpy.kalman import KalmanFilter\n",
+      "import book_plots as bp\n",
       "\n",
       "def plot_rts(noise):\n",
       "    random.seed(123)\n",
@@ -538,10 +539,10 @@
       "    M,P,C = fk.rts_smoother(mu, cov)\n",
       "\n",
       "    # plot data\n",
-      "    plt.plot(zs,'r', marker='o', linewidth=1, alpha=0.5, label='measurement')\n",
+      "    bp.plot_measurements(zs, lw=1)\n",
       "    plt.plot(M[:, 0], c='b', label='RTS')\n",
       "    plt.plot(mu[:, 0], c='g', linestyle='--', label='KF output')\n",
-      "    plt.plot([0, len(zs)], [0, len(zs)], 'k', linewidth=1, label='ideal') \n",
+      "    plt.plot([0, len(zs)], [0, len(zs)], 'k', linewidth=2, label='track') \n",
       "    plt.legend(loc=4)\n",
       "\n",
       "    plt.show()\n",
@@ -554,9 +555,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f36bc94fc18>"
+        "<matplotlib.figure.Figure at 0x7f63a3c7d710>"
        ]
       }
      ],
@@ -583,9 +584,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f36bc996eb8>"
+        "<matplotlib.figure.Figure at 0x7f63a3ca4780>"
        ]
       }
      ],

15_Adaptive_Filtering.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:96201d2c9d8ab085f9c9aa24bd26c0287ee00d2e33a90966c38cda8a4c88c8e3"
+  "signature": "sha256:ea9ba1062dc60729d48568820c87376591663e20618d16c4b408d668f2c2c117"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -259,7 +259,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7f218207a9e8>"
+        "<IPython.core.display.HTML at 0x7f93b63326a0>"
        ]
       }
      ],
@@ -402,6 +402,7 @@
      "collapsed": false,
      "input": [
       "from numpy import array\n",
+      "import book_plots as bp\n",
       "\n",
       "noise_factor = 2.\n",
       "\n",
@@ -429,7 +430,7 @@
       "    return pos, zs\n",
       "\n",
       "pos, zs = generate_data(50, noise_factor)\n",
-      "plt.plot(*zip(*pos))\n",
+      "bp.plot_track(*zip(*pos))\n",
       "plt.axis('equal')\n",
       "plt.xlabel('X')\n",
       "plt.ylabel('Y')\n",
@@ -442,9 +443,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f21654d0e48>"
+        "<matplotlib.figure.Figure at 0x7f9399e84fd0>"
        ]
       }
      ],
@@ -461,8 +462,8 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "plt.plot(*zip(*pos), lw=4, label='track')\n",
-      "plt.plot(*zip(*zs), label='measurements')\n",
+      "bp.plot_track(*zip(*pos))\n",
+      "bp.plot_measurements(*zip(*zs))\n",
       "plt.axis('equal')\n",
       "plt.legend(loc=4)\n",
       "plt.xlabel('X')\n",
@@ -476,9 +477,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f21651eb940>"
+        "<matplotlib.figure.Figure at 0x7f9399ba8ac8>"
        ]
       }
      ],
@@ -562,8 +563,8 @@
       "# plot the results\n",
       "kxs, _, _, _ = cvfilter.batch_filter(z_xs)\n",
       "t = np.arange(0, len(z_xs) * dt, dt)\n",
-      "plt.plot(t, xs, label='track')\n",
-      "plt.plot(t, kxs[:,0], label='KF')\n",
+      "bp.plot_track(t, xs)\n",
+      "bp.plot_filter(t, kxs[:,0], label='KF')\n",
       "plt.xlabel('time (sec)')\n",
       "plt.ylabel('X')\n",
       "plt.legend(loc=4)\n",
@@ -576,9 +577,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f2164fc9f98>"
+        "<matplotlib.figure.Figure at 0x7f939996b198>"
        ]
       }
      ],
@@ -605,8 +606,8 @@
       "# plot the results\n",
       "kxs2, _, _, _ = cvfilter.batch_filter(z_xs2)\n",
       "\n",
-      "plt.plot(t, xs2, label='track')\n",
-      "plt.plot(t, kxs2[:,0], label='filter')\n",
+      "bp.plot_track(t, xs2)\n",
+      "bp.plot_filter(t, kxs2[:,0], label='KF')\n",
       "plt.xlabel('time (sec)')\n",
       "plt.ylabel('X')\n",
       "plt.title('Effects of Acceleration')\n",
@@ -619,9 +620,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f21650e2b00>"
+        "<matplotlib.figure.Figure at 0x7f9399b9c198>"
        ]
       }
      ],
@@ -645,8 +646,8 @@
       "# recompute track\n",
       "kxs2, _, _, _ = cvfilter.batch_filter(z_xs2)\n",
       "\n",
-      "plt.plot(t, xs2, label='track')\n",
-      "plt.plot(t, kxs2[:,0], label='filter')\n",
+      "bp.plot_track(t, xs2)\n",
+      "bp.plot_filter(t, kxs2[:,0], label='KF')\n",
       "plt.legend(loc=4)\n",
       "plt.xlabel('time (sec)')\n",
       "plt.ylabel('X')\n",
@@ -659,9 +660,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f21651ac5f8>"
+        "<matplotlib.figure.Figure at 0x7f939584ff98>"
        ]
       }
      ],
@@ -686,8 +687,8 @@
       "cvfilter.x = array([0., 0.])\n",
       "kxs2, _, _, _ = cvfilter.batch_filter(z_xs2)\n",
       "\n",
-      "plt.plot(t, xs2, label='track')\n",
-      "plt.plot(t, kxs2[:,0], label='filter')\n",
+      "bp.plot_track(t, xs2)\n",
+      "bp.plot_filter(t, kxs2[:,0], label='KF')\n",
       "plt.legend(loc=4)\n",
       "plt.xlabel('time (sec)')\n",
       "plt.ylabel('X')\n",
@@ -700,9 +701,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f2164f38128>"
+        "<matplotlib.figure.Figure at 0x7f93998f0358>"
        ]
       }
      ],
@@ -746,8 +747,8 @@
       "initialize_const_accel(cafilter)\n",
       "\n",
       "kxs2, _, _, _ = cafilter.batch_filter(z_xs2)\n",
-      "plt.plot(t, xs2, label='track')\n",
-      "plt.plot(t, kxs2[:,0], label='filter')\n",
+      "bp.plot_track(t, xs2)\n",
+      "bp.plot_filter(t, kxs2[:,0], label='KF')\n",
       "plt.legend(loc=4)\n",
       "plt.xlabel('time (sec)')\n",
       "plt.ylabel('X')\n",
@@ -760,9 +761,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f2164f146a0>"
+        "<matplotlib.figure.Figure at 0x7f93998cf048>"
        ]
       }
      ],
@@ -811,9 +812,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f2164e79668>"
+        "<matplotlib.figure.Figure at 0x7f93957f8e48>"
        ]
       }
      ],
@@ -846,8 +847,8 @@
       "    res.append(cvfilter.y[0])\n",
       "xs = np.asarray(xs)\n",
       "plt.subplot(121)\n",
-      "plt.plot(t, z_xs2, label='z')\n",
-      "plt.plot(t, xs, label='filter')\n",
+      "bp.plot_measurements(t, z_xs2, label='z')\n",
+      "bp.plot_filter(t, xs)\n",
       "plt.legend(loc=2)\n",
       "plt.xlabel('time (sec)')\n",
       "plt.ylabel('X')\n",
@@ -867,9 +868,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f21651ac6d8>"
+        "<matplotlib.figure.Figure at 0x7f93998d2048>"
        ]
       }
      ],
@@ -973,9 +974,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f2164f69be0>"
+        "<matplotlib.figure.Figure at 0x7f939984e8d0>"
        ]
       }
      ],
@@ -1027,8 +1028,8 @@
       "        cvfilter.Q /= Q_scale_factor\n",
       "        count -= 1\n",
       "        \n",
-      "plt.plot(t, z_xs2, label='z')\n",
-      "plt.plot(t, xs, label='filter')\n",
+      "bp.plot_measurements(t, z_xs2, lw=6, label='z')\n",
+      "bp.plot_filter(t, xs, label='filter')\n",
       "plt.ylabel('$\\epsilon$')\n",
       "plt.xlabel('time (sec)')\n",
       "plt.legend(loc=4)\n",
@@ -1041,9 +1042,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f215d51b860>"
+        "<matplotlib.figure.Figure at 0x7f9395841320>"
        ]
       }
      ],
@@ -1126,8 +1127,8 @@
       "\n",
       "    xs = np.asarray(xs)\n",
       "    plt.subplot(121)\n",
-      "    plt.plot(t, z_xs2, label='z')\n",
-      "    plt.plot(t, xs[:,0], label='filter')\n",
+      "    bp.plot_measurements(t, z_xs2, label='z')\n",
+      "    bp.plot_filter(t, xs[:,0])\n",
       "    plt.ylabel('$\\epsilon$')\n",
       "    plt.xlabel('time (sec)')\n",
       "    plt.legend(loc=2)\n",
@@ -1159,9 +1160,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f2164f94fd0>"
+        "<matplotlib.figure.Figure at 0x7f93957a27f0>"
        ]
       }
      ],
@@ -1193,9 +1194,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f2164fa8198>"
+        "<matplotlib.figure.Figure at 0x7f93998a7198>"
        ]
       }
      ],
@@ -1220,9 +1221,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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        "text": [
-        "<matplotlib.figure.Figure at 0x7f21651ac710>"
+        "<matplotlib.figure.Figure at 0x7f9399982710>"
        ]
       }
      ],
@@ -1248,17 +1249,17 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f2164ecfd68>"
+        "<matplotlib.figure.Figure at 0x7f9391e42fd0>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f215d22c860>"
+        "<matplotlib.figure.Figure at 0x7f9391ea0a20>"
        ]
       }
      ],
@@ -1289,41 +1290,41 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f215d7a7710>"
+        "<matplotlib.figure.Figure at 0x7f939985dba8>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f215d7a7b70>"
+        "<matplotlib.figure.Figure at 0x7f939985d668>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f2166060cf8>"
+        "<matplotlib.figure.Figure at 0x7f9399960048>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f215d52b400>"
+        "<matplotlib.figure.Figure at 0x7f9391d36a20>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f215d270ef0>"
+        "<matplotlib.figure.Figure at 0x7f9391d36128>"
        ]
       }
      ],
@@ -1401,7 +1402,7 @@
       "    res.append(cvfilter.y[0])\n",
       "xs = np.asarray(xs)\n",
       "plt.subplot(221)\n",
-      "plt.plot(t[0:100], z_xs2, label='measurement', alpha=0.5)\n",
+      "bp.plot_measurements(t[0:100], z_xs2, label='z')\n",
       "plt.plot(t[0:100], xs, label='filter')\n",
       "plt.legend(loc=2)\n",
       "plt.title('Standard Kalman Filter')\n",
@@ -1421,7 +1422,7 @@
       "xs = np.asarray(xs)\n",
       "\n",
       "plt.subplot(222)\n",
-      "plt.plot(t[0:100], z_xs2, label='z', alpha=0.5)\n",
+      "bp.plot_measurements(t[0:100], z_xs2, label='z')\n",
       "plt.plot(t[0:100], xs, label='filter')\n",
       "plt.legend(loc=2)\n",
       "plt.title('$\\\\mathbf{Q}=20$')\n",
@@ -1440,7 +1441,7 @@
       "    res.append(cvfilter.y[0])\n",
       "xs = np.asarray(xs)\n",
       "plt.subplot(223)\n",
-      "plt.plot(t[0:100], z_xs2, label='z', alpha=0.5)\n",
+      "bp.plot_measurements(t[0:100], z_xs2, label='z')\n",
       "plt.plot(t[0:100], xs, label='filter')\n",
       "plt.legend(loc=2)\n",
       "plt.title('Fading Memory ($\\\\alpha$ = 1.02)')\n",
@@ -1458,7 +1459,7 @@
       "    res.append(cvfilter.y[0])\n",
       "xs = np.asarray(xs)\n",
       "plt.subplot(224)\n",
-      "plt.plot(t[0:100], z_xs2, label='z', alpha=0.5)\n",
+      "bp.plot_measurements(t[0:100], z_xs2, label='z')\n",
       "plt.plot(t[0:100], xs, label='filter')\n",
       "plt.legend(loc=2)\n",
       "plt.title('Fading Memory ($\\\\alpha$ = 1.05)')\n",
@@ -1471,9 +1472,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f216518ceb8>"
+        "<matplotlib.figure.Figure at 0x7f939994ff60>"
        ]
       }
      ],

animations/Gaussians_Animations.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:456e95ad9d347f3ae0b9b1ebfb1a51829b89dcde3421170630fc3387515bda9c"
+  "signature": "sha256:67b73f8366759bb2837043e5a461a7ce4ff077967115f36e8017eaa1318f1c0f"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -16,7 +16,7 @@
       "%matplotlib inline\n",
       "import sys\n",
       "sys.path.insert(0,'..') # allow us to format the book\n",
-      "\n",
+      "sys.path.insert(0,'../code') \n",
       "# use same formatting as rest of book so that the plots are\n",
       "# consistant with that look and feel.\n",
       "import book_format\n",
@@ -245,13 +245,13 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 1,
+       "prompt_number": 3,
        "text": [
-        "<IPython.core.display.HTML at 0x7fde2b0ceb38>"
+        "<IPython.core.display.HTML at 0x7fc8b024ce80>"
        ]
       }
      ],
-     "prompt_number": 1
+     "prompt_number": 3
     },
     {
      "cell_type": "code",
@@ -268,7 +268,7 @@
       "    plt.plot (xs, ys)\n",
       "    plt.ylim((0,1))\n",
       "\n",
-      "    \n",
+      "\n",
       "mu = 2\n",
       "sigma = 0.6\n",
       "def ganimate(frame):\n",
@@ -293,13 +293,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAcEAAAGrCAYAAAC41CzUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAcEAAAGrCAYAAAC41CzUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fde0e233e80>"
+        "<matplotlib.figure.Figure at 0x7fc8aff6f9e8>"
        ]
       }
      ],
-     "prompt_number": 2
+     "prompt_number": 4
     },
     {
      "cell_type": "code",
@@ -314,7 +314,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "<img src='gaussian_animate.gif'>"
+      "<img src='04_gaussian_animate.gif'>"
      ]
     }
    ],

animations/Kalman_Filters_Animations.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:8dfeca3728647e0419c6f2c7f141dc89f294f6d749e8e29d6baf3d93643fb2a2"
+  "signature": "sha256:bacbdf37e5f8b4f5a0841d27be72d89c6cfab901e53ba2bc642b422618558c14"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -16,6 +16,7 @@
       "%matplotlib inline\n",
       "import sys\n",
       "sys.path.insert(0,'..') # allow us to format the book\n",
+      "sys.path.insert(0,'../code') \n",
       "\n",
       "# use same formatting as rest of book so that the plots are\n",
       "# consistant with that look and feel.\n",
@@ -35,7 +36,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 3
+     "prompt_number": 1
     },
     {
      "cell_type": "markdown",
@@ -102,7 +103,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 4
+     "prompt_number": 2
     },
     {
      "cell_type": "code",
@@ -171,19 +172,19 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa8ae47c7f0>"
+        "<matplotlib.figure.Figure at 0x7f2d5f871b70>"
        ]
       }
      ],
-     "prompt_number": 5
+     "prompt_number": 3
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "<img src='volt_animate.gif'>"
+      "<img src='05_volt_animate.gif'>"
      ]
     },
     {
@@ -247,13 +248,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAcYAAAHFCAYAAABo9lmHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa8a6b732e8>"
+        "<matplotlib.figure.Figure at 0x7f2d585b7e10>"
        ]
       }
      ],
-     "prompt_number": 6
+     "prompt_number": 4
     },
     {
      "cell_type": "markdown",

animations/discrete_bayes_animations.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:433f3b1a6dada8b107fd2ee6433183f04ca080c6c5ef055d0f83e14f9c714fb3"
+  "signature": "sha256:8f58b707cf76c184d840e41abdb14f1e584995252c4df05a742336b9f07b9b0b"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -24,6 +24,7 @@
       "import matplotlib.pyplot as plt\n",
       "import sys\n",
       "sys.path.insert(0,'..') # allow us to format the book\n",
+      "sys.path.insert(0,'../code') \n",
       "import book_format\n",
       "book_format.load_style(directory='..')"
      ],
@@ -250,13 +251,13 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 2,
+       "prompt_number": 8,
        "text": [
-        "<IPython.core.display.HTML at 0x7ff8d0752630>"
+        "<IPython.core.display.HTML at 0x7f8ada8a3d30>"
        ]
       }
      ],
-     "prompt_number": 2
+     "prompt_number": 8
     },
     {
      "cell_type": "markdown",
@@ -272,25 +273,13 @@
       "from matplotlib import animation\n",
       "import matplotlib.pyplot as plt\n",
       "import numpy as np\n",
-      "%matplotlib inline\n",
-      "\n",
-      "def barplot(pos, ylim=(0,1)):\n",
-      "    \"\"\" implement plotting of bar plots in a way that plays nicely with the\n",
-      "    animations.\"\"\"\n",
-      "    \n",
-      "    plt.cla()\n",
-      "    ax = plt.gca()\n",
-      "    x = np.arange(len(pos))\n",
-      "    ax.bar(x, pos)\n",
-      "    if ylim:\n",
-      "        plt.ylim(ylim)\n",
-      "    plt.xticks(x+0.4, x)\n",
-      "    plt.grid()"
+      "from book_plots import bar_plot\n",
+      "%matplotlib inline"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 3
+     "prompt_number": 9
     },
     {
      "cell_type": "code",
@@ -327,7 +316,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 4
+     "prompt_number": 10
     },
     {
      "cell_type": "code",
@@ -336,7 +325,7 @@
       "pos = [1.0,0,0,0,0,0,0,0,0,0]\n",
       "def bar_animate(nframe):\n",
       "    global pos\n",
-      "    barplot(pos)\n",
+      "    bar_plot(pos)\n",
       "    pos = predict(pos, 1, .8, .1, .1)\n",
       "\n",
       "\n",
@@ -351,13 +340,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAbgAAACsCAYAAAAJ+rmKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nOElSkyw4SVKTLDhJUpMsOElSk/4Po5u82b9IDysAAAAASUVORK5CYII=\n",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAbgAAACsCAYAAAAJ+rmKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ff8d0768198>"
+        "<matplotlib.figure.Figure at 0x7f8ada768cf8>"
        ]
       }
      ],
-     "prompt_number": 5
+     "prompt_number": 11
     },
     {
      "cell_type": "markdown",
@@ -378,7 +367,7 @@
       "    if nframe == 0:\n",
       "        return\n",
       "\n",
-      "    barplot(pos, ylim=(0,1.0))\n",
+      "    bar_plot(pos, ylim=(0,1.0))\n",
       "    if nframe % 2 == 0:\n",
       "        pos = predict(pos, 1, .8, .1, .1)\n",
       "    else:\n",
@@ -400,11 +389,11 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAbgAAACsCAYAAAAJ+rmKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAADXJJREFUeJzt3X2QXXddx/H3pwm1BMpW6FgkXSYokZRRbHlIOyAPSpSk\naquMY41UxypYR4M4jlrgD/zDGRn+YGSYYifUtlMUGocCGsYmZRBEEKwN9IHSrJMUdkwCVJ66DA91\nEvn6xz2Nl+1m7+7mbs7Nr+/XTCb3nPPL3s/sZvdzf79zzt1UFZIkteaMvgNIkrQaLDhJUpMsOElS\nkyw4SVKTLDhJUpMsOElSk0YWXJIbkzyY5LOLjHl7kgNJ7kly0XgjSpK0fEuZwd0EbD3RwSSXAs+s\nqo3A7wLXjSmbJEkrNrLgqurjwDcWGXIZcHM39g7gnCTnjSeeJEkrs3YMH2M9cGho+zBwPvDgIzvm\n5uZ8uxRJ0qqZmprK/H3jushk/ge20CRJvRpHwR0Bpoe2z+/2SZLUm3EsUe4GdgC7klwCPFRVD55o\n8NTU1BieUpL0WDc3N7fo8ZEFl+QW4KXAuUkOAX8OPA6gqnZW1W1JLk1yEPg2cNVJp5Yk6STlVPy6\nnOGLTJzBSZLGYXgGt5oXmUiSNFEsOElSkyw4SVKTLDhJUpMsOElSkyw4SVKTLDhJUpMsOElSkyw4\nSVKTLDhJUpMsOElSkyw4SVKTLDhJUpMsOElSkyw4SVKTLDhJUpMsOElSkyw4SVKTLDhJUpNGFlyS\nrUlmkhxIcs0Cx89NsjfJ3UnuS/Jbq5JUkqRlWLTgkqwBrgW2As8Gtie5YN6wHcBdVXUh8DLgrUnW\nrkJWSZKWbNQMbjNwsKpmq+oosAu4fN6YLwFP6h4/CfhaVR0bb0xJkpZn1ExrPXBoaPswcPG8MdcD\nH0nyReBs4FfHF0+SpJUZNYOrJXyMNwJ3V9XTgAuBdyQ5+6STSZJ0EkYV3BFgemh7msEsbtgLgfcC\nVNUDwBeAZ40roCRJKzGq4PYBG5NsSHImcAWwe96YGWALQJLzGJTb58cdVJKk5Vj0HFxVHUuyA7gd\nWAPcUFX7k1zdHd8J/CVwU5J7GBTmn1XV11c5tyRJi0rVUk6znZy5ubnjTzI1NbXqzydJat/c3Nzx\nx1NTU5l/3HcykSQ1yYKTJDXJgpMkNcmCkyQ1yYKTJDXJgpMkNcmCkyQ1yYKTJDXJgpMkNcmCkyQ1\nyYKTJDXJgpMkNcmCkyQ1yYKTJDXJgpMkNcmCkyQ1yYKTJDXJgpMkNcmCkyQ1aWTBJdmaZCbJgSTX\nnGDMy5LcleS+JP8y9pSSJC3T2sUOJlkDXAtsAY4AdybZXVX7h8acA7wDeEVVHU5y7moGliRpKUbN\n4DYDB6tqtqqOAruAy+eN+XXgfVV1GKCqvjr+mJIkLc+oglsPHBraPtztG7YReHKSjybZl+Q3xhlQ\nkqSVWHSJEqglfIzHAc8FXg6sAz6V5N+r6sDJhpMkaaVGFdwRYHpoe5rBLG7YIeCrVfVd4LtJ/hX4\nScCCkyT1ZtQS5T5gY5INSc4ErgB2zxvzj8BPJVmTZB1wMXD/+KNKkrR0i87gqupYkh3A7cAa4Iaq\n2p/k6u74zqqaSbIXuBf4HnB9VVlwkqRepWopp9lOztzc3PEnmZqaWvXnkyS1b25u7vjjqampzD/u\nO5lIkppkwUmSmmTBSZKaZMFJkppkwUmSmjTqRm9J0mPUzMzDzM72m2HDBti06awV/VsLTpK0oNlZ\n2LZtZeUyLnv2PMymTSv7ty5RSpKaZMFJkppkwUmSmmTBSZKaZMFJkppkwUmSmmTBSZKaZMFJkppk\nwUmSmmTBSZKaZMFJkppkwUmSmjSy4JJsTTKT5ECSaxYZ94Ikx5K8crwRJUlavkULLska4FpgK/Bs\nYHuSC04w7i3AXiCrkFOSpGUZNYPbDBysqtmqOgrsAi5fYNxrgVuBr4w5nyRJKzKq4NYDh4a2D3f7\njkuynkHpXdftqrGlkyRphUYV3FLK6m3A66uqGCxPukQpSerdqN/ofQSYHtqeZjCLG/Y8YFcSgHOB\nbUmOVtXusaWUJGmZRhXcPmBjkg3AF4ErgO3DA6rqRx55nOQm4IOWmySpb4sWXFUdS7IDuB1YA9xQ\nVfuTXN0d33kKMkqStGyjZnBU1R5gz7x9CxZbVV01plySJJ0U38lEktQkC06S1CQLTpLUJAtOktQk\nC06S1CQLTpLUJAtOktQkC06S1CQLTpLUJAtOktQkC06S1CQLTpLUJAtOktQkC06S1CQLTpLUJAtO\nktQkC06S1CQLTpLUJAtOktSkJRVckq1JZpIcSHLNAsdfleSeJPcm+bckzxl/VEmSlm5kwSVZA1wL\nbAWeDWxPcsG8YZ8HXlJVzwH+AnjnuINKkrQcS5nBbQYOVtVsVR0FdgGXDw+oqk9V1Vy3eQdw/nhj\nSpK0PEspuPXAoaHtw92+E/kd4LaTCSVJ0slau4QxtdQPluSngd8GXrTiRJIkjcFSCu4IMD20Pc1g\nFvd9ugtLrge2VtU3xhNPkqSVWcoS5T5gY5INSc4ErgB2Dw9I8nTg/cCVVXVw/DElSVqekTO4qjqW\nZAdwO7AGuKGq9ie5uju+E3gT8IPAdUkAjlbV5tWLLUmnv5mZh5md7e/5N2yATZvO6i/AKlvKEiVV\ntQfYM2/fzqHHrwZePd5oktS22VnYtq2/gtmz52E2bert6Ved72QiSWqSBSdJapIFJ0lqkgUnSWqS\nBSdJatKSrqIcp717Hz7VT3lc65fESpL+3ykvOC+JlSSdCi5RSpKaZMFJkppkwUmSmmTBSZKaZMFJ\nkpp0yq+inGS+s7cktcOCG+I7e0tSO1yilCQ1yYKTJDXJJUpJTer7nDp4Xr1vFpykJvV9Th08r943\nlyglSU0aWXBJtiaZSXIgyTUnGPP27vg9SS4af0xJkpZn0SXKJGuAa4EtwBHgziS7q2r/0JhLgWdW\n1cYkFwPXAZesYubHLM8pSNLSjToHtxk4WFWzAEl2AZcD+4fGXAbcDFBVdyQ5J8l5VfXgKuR9TPOc\ngiaFL7Z0OkhVnfhg8ivAK6rqNd32lcDFVfXaoTEfBN5cVZ/stj8MXFNVn35kzNzc3ImfRJKkkzQ1\nNZX5+0adg1tqMc3/wBaaJKlXowruCDA9tD0NHB4x5vxunyRJvRl1Dm4fsDHJBuCLwBXA9nljdgM7\ngF1JLgEemn/+baGpoyRJq2nRgquqY0l2ALcDa4Abqmp/kqu74zur6rYklyY5CHwbuGrVU0uSNMKi\nF5lIknS6Oi3eyWQpN5v3KcmNSR5M8tm+sywkyXSSjyb5XJL7kvxh35nmS3JWkjuS3J3k/iRv7jvT\nQpKsSXJXd/XwxEkym+TeLuN/9J1nId2tRLcm2d99rSfqvtkkz+o+f4/8mZu075kkb+i+nz+b5D1J\nfqDvTPMleV2X774kr+slw6TP4Lqbzf+ToZvNge3DN5v3LcmLgW8B76qqn+g7z3xJngo8taruTvJE\n4NPAL03S5xAgybqq+k6StcAngD+pqk/0nWtYkj8GngecXVWX9Z1nviRfAJ5XVV/vO8uJJLkZ+FhV\n3dh9rZ9QVXN951pIkjMY/NzZXFWH+s4D0F0T8RHggqr6nyR/D9xWVTf3GmxIkh8HbgFeABwF9gK/\nV1UPnMocp8MM7vjN5lV1FHjkZvOJUVUfB77Rd44TqaovV9Xd3eNvMbhR/2n9pnq0qvpO9/BMBud8\nJ+qHdJLzgUuBv+HRt8ZMkonNlmQKeHFV3QiD8/yTWm6dLcADk1JunW8yKI113QuEdUzeleubgDuq\n6uGq+l/gY8ArT3WI06Hg1gPD/7kOd/u0At2rv4uAO/pN8mhJzkhyN/Ag8NGqur/vTPP8FfCnwPf6\nDrKIAj6cZF+S1/QdZgHPAL6S5KYkn0lyfZJ1fYdaxK8B7+k7xLBudv5W4L8YXN3+UFV9uN9Uj3If\n8OIkT+6+vj/P4BayU+p0KLjJXkM9jXTLk7cCr+tmchOlqr5XVRcy+EZ4SZKX9RzpuCS/APx3Vd3F\nBM+QgBdV1UXANuAPuuXzSbIWeC7w11X1XAZXXr++30gLS3Im8IvAe/vOMizJjwJ/BGxgsBLzxCSv\n6jXUPFU1A7wF+BCwB7iLHl4Yng4Ft5SbzTVCkscB7wP+rqr+oe88i+mWrP4JeH7fWYa8ELisO8d1\nC/AzSd7Vc6ZHqaovdX9/BfgAgyX+SXIYOFxVd3bbtzIovEm0Dfh097mcJM8HPllVX6uqY8D7Gfz/\nnChVdWNVPb+qXgo8xOBailPqdCi44zebd6+ormBwc7mWKEmAG4D7q+ptfedZSJJzk5zTPX488LMM\nXvVNhKp6Y1VNV9UzGCxbfaSqfrPvXMOSrEtydvf4CcDPARN1ZW9VfRk4lOTHul1bgM/1GGkx2xm8\nmJk0M8AlSR7ffW9vASZtOZ8kP9T9/XTgl+lhqXfif6P3iW427znW90lyC/BS4ClJDgFvqqqbeo41\n7EXAlcC9SR4pjTdU1d4eM833w8DN3VVrZwB/W1X/3HOmxUzi0vl5wAcGP/NYC7y7qj7Ub6QFvRZ4\nd/eC9QEm8M0huhcIW4CJO49ZVfd0qwf7GCz7fQZ4Z7+pFnRrkqcwuCDm96vqm6c6wMTfJiBJ0kqc\nDkuUkiQtmwUnSWqSBSdJapIFJ0lqkgUnSWqSBSdJapIFJ0lq0v8ByVyjNXG2/lYAAAAASUVORK5C\nYII=\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x7ff8d065cfd0>"
+        "<matplotlib.figure.Figure at 0x7f8ada701d30>"
        ]
       }
      ],
-     "prompt_number": 6
+     "prompt_number": 14
     },
     {
      "cell_type": "markdown",

code/538.json

@@ -4,12 +4,18 @@
   "patch.linewidth": 0.5,
   "legend.fancybox": true,
   "axes.color_cycle": [
-    "#30a2da",
-    "#fc4f30",
     "#6d904f",
-    "#8b8b8b",
-    "#e5ae38"
-
+    "#013afe", 
+    "#202020",
+    "#fc4f30",
+    "#e5ae38",
+    "#A60628",
+    "#30a2da",
+    "#008080",
+    "#7A68A6",
+    "#CF4457",
+    "#188487",
+    "#E24A33"
   ],
   "axes.facecolor": "#ffffff",
   "axes.labelsize": "large",
@@ -21,7 +27,7 @@
   "examples.directory": "",
   "figure.facecolor": "#ffffff",
   "grid.linestyle": "-",
-  "grid.linewidth": 1.0,
+  "grid.linewidth": 2.0,
   "grid.color": "#cbcbcb",
   "axes.edgecolor":"#f0f0f0",
   "xtick.major.size": 0,
@@ -30,12 +36,14 @@
   "ytick.minor.size": 0,
   "axes.linewidth": 3.0,
   "font.size":14.0,
-  "lines.linewidth": 2,
+  "lines.linewidth": 4,
   "lines.solid_capstyle": "butt",
   "savefig.edgecolor": "#f0f0f0",
   "savefig.facecolor": "#f0f0f0",
   "figure.subplot.left"    : 0.08,
   "figure.subplot.right"   : 0.95, 
   "figure.subplot.bottom"  : 0.07,
-  "figure.subplot.hspace"  : 0.5
+  "figure.subplot.hspace"  : 0.5,
+  "legend.scatterpoints" : 1
 }
+

code/bar_plot.py

@@ -1,26 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Created on Fri May  2 12:21:40 2014
-
-@author: rlabbe
-"""
-import matplotlib.pyplot as plt
-import numpy as np
-
-def plot(pos, ylim=(0,1), title=None):
-    plt.cla()
-    ax = plt.gca()
-    x = np.arange(len(pos))
-    ax.bar(x, pos)
-    if ylim:
-        plt.ylim(ylim)
-    plt.xticks(x+0.4, x)
-    plt.grid()
-    if title is not None:
-        plt.title(title)
-
-
-if __name__ == "__main__":
-    p = [0.2245871, 0.06288015, 0.06109133, 0.0581008, 0.09334062, 0.2245871,
-     0.06288015, 0.06109133, 0.0581008,  0.09334062]*2
-    plot(p)

code/book_plots.py

@@ -0,0 +1,74 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Fri May  2 12:21:40 2014
+
+@author: rlabbe
+"""
+import matplotlib.pyplot as plt
+import numpy as np
+
+def bar_plot(pos, ylim=(0,1), title=None):
+    plt.cla()
+    ax = plt.gca()
+    x = np.arange(len(pos))
+    ax.bar(x, pos, color='#30a2da')
+    if ylim:
+        plt.ylim(ylim)
+    plt.xticks(x+0.4, x)
+    if title is not None:
+        plt.title(title)
+
+
+def plot_measurements(xs, ys=None, c='r', lw=2, label='Measurements', **kwargs):
+    """ Helper function to give a consistant way to display
+    measurements in the book.
+    """
+
+    plt.autoscale(tight=True)
+    '''if ys is not None:
+        plt.scatter(xs, ys, marker=marker, c=c, s=s,
+                    label=label, alpha=alpha)
+        if connect:
+           plt.plot(xs, ys, c=c, lw=1, alpha=alpha)
+    else:
+        plt.scatter(range(len(xs)), xs, marker=marker, c=c, s=s,
+                    label=label, alpha=alpha)
+        if connect:
+           plt.plot(range(len(xs)), xs, lw=1, c=c, alpha=alpha)'''
+
+    if ys is not None:
+        plt.plot(xs, ys, c=c, lw=lw, linestyle='--', label=label, **kwargs)
+    else:
+        plt.plot(xs, c=c, lw=lw, linestyle='--', label=label, **kwargs)
+
+
+
+def plot_residual_limits(Ps):
+    std = np.sqrt(Ps)
+
+    plt.plot(-std, c='k', ls=':', lw=2)
+    plt.plot(std, c='k', ls=':', lw=2)
+    plt.fill_between(range(len(std)), -std, std,
+                 facecolor='#ffff00', alpha=0.3)
+
+
+def plot_track(xs, ys=None, label='Track', c='k', lw=2):
+    if ys is not None:
+        plt.plot(xs, ys, c=c, lw=lw, label=label)
+    else:
+        plt.plot(xs, c=c, lw=lw, label=label)
+
+
+#c='#013afe'
+def plot_filter(xs, ys=None, c='#6d904f', label='Filter', **kwargs):
+    if ys is not None:
+        plt.plot(xs, ys, c=c, label=label, **kwargs)
+    else:
+        plt.plot(xs, c=c, label=label, **kwargs)
+
+
+if __name__ == "__main__":
+    p = [0.2245871, 0.06288015, 0.06109133, 0.0581008, 0.09334062, 0.2245871,
+     0.06288015, 0.06109133, 0.0581008,  0.09334062]*2
+    bar_plot(p)
+    plot_measurements(p)